Dissertations / Theses: 'Multidimensional scaling'

1

Bell, Paul W. "Statistical inference for multidimensional scaling." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327197.

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CUGNATA, FEDERICA. "Bayesian three-way multidimensional scaling." Doctoral thesis, Università Bocconi, 2012. https://hdl.handle.net/11565/4054285.

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3

Jones, Synthia S. "Multidimensional scaling of user information satisfaction." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA277230.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993.
Thesis advisor(s): William J. Haga ; Kishore Sengupta. "December 1993." Bibliography: p. 108-110. Also available online.

4

Ingram, Stephen F. "Multilevel multidimensional scaling on the GPU." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/409.

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We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm. We also propose a novel texture paging strategy called distance paging for working with precomputed distance matrices too large to fit in texture memory.

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McQuaid, Michael J., Thian-Huat Ong, Hsinchun Chen, and Jay F. Nunamaker. "Multidimensional scaling for group memory visualization." Elsevier, 1999. http://hdl.handle.net/10150/105458.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
We describe an attempt to overcome information overload through information visualization â in a particular domain, group memory. A brief review of information visualization is followed by a brief description of our methodology. We . discuss our system, which uses multidimensional scaling MDS to visualize relationships between documents, and which . we tested on 60 subjects, mostly students. We found three important and statistically significant differences between task performance on an MDS-generated display and on a randomly generated display. With some qualifications, we conclude that MDS speeds up and improves the quality of manual classification of documents and that the MDS display agrees with subject perceptions of which documents are similar and should be displayed together.

6

Tulabandula, Sridhar. "Localization of wireless sensor networks using multidimensional scaling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4986.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 17, 2008) Includes bibliographical references.

7

Sun, Jiang. "Extending the metric multidimensional scaling with bregman divergences." Thesis, University of the West of Scotland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556070.

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8

Mohd, Yunus Mohd Yusri. "Multivariate statistical process monitoring using classical multidimensional scaling." Thesis, University of Newcastle upon Tyne, 2012. http://hdl.handle.net/10443/1495.

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A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linearbased Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternatively, the proposed method utilizes the inter-dissimilarity scales in describing the relationships among the monitored variables instead of variance-covariance measure for the multivariate scores development. However, the scores are plotted in terms of variable structure, thus providing different formulation of statistics for monitoring. Nonetheless, the proposed statistics still correspond to the conceptual objective of Hotelling’s T2 and Squared Prediction Errors (SPE). The first framework corresponds to the original CMDS framework, whereas the second utilizes Procrustes Analysis (PA) functions which is analogous to the concept of loading factors in PCA for score projection. Lastly, the final framework employs dynamic mechanism of PA functions as an alternative for enhancing the procedures of the second approach. A simulated system of Continuous Stirred Tank Reactor with Recycle (CSTRwR) has been chosen for the demonstration and the fault detection results were comparatively analyzed to the outcomes of PCA on the grounds of false alarm rates, total number of detected cases and also total number of fastest detection cases. The last two performance factors are obtained through fault detection time. The overall outcomes show that the three CMDS-based systems give almost comparable performances to the linear PCA based monitoring systemwhen dealing the abrupt fault events, whereas the new systems have demonstrated significant improvement over the conventional method in detecting incipient fault cases. More importantly, this monitoring accomplishment can be efficiently executed based on lower compressed dimensional space compared to the PCA technique, thus providing much simpler solution. All of these evidences verified that the proposed approaches are successfully developed conceptually as well as practically for monitoring while complying fundamentally with the principles and technical steps of the conventional MSPM system.

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Williams, Michelle A. "A factor structure with means confirmatory factor analytic approach to multitrait-multimethod models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5010.

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Thesis (M.A.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 6, 2007) Includes bibliographical references.

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Jansson, Mattias, and Jimmy Johansson. "Interactive Visualization of Statistical Data using Multidimensional Scaling Techniques." Thesis, Linköping University, Department of Science and Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1716.

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This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137.

In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions.

In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize.

Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.

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Mair, Patrick, Ingwer Borg, and Thomas Rusch. "Goodness-of-Fit Assessment in Multidimensional Scaling and Unfolding." Taylor & Francis Group, 2016. http://epub.wu.ac.at/5354/1/mairetal2016.pdf.

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Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.

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McGlynn, Marion. "The performance of three fitting criteria for multidimensional scaling /." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59652.

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A Monte Carlo study was performed to investigate the ability of MSCAL to recover by Euclidean metric multi-dimensional scaling (MDS) the true structure for dissimilarity data with different underlying error distributions. Error models for three typical error distributions: normal, lognormal, and squared normal are implemented in MSCAL through data transformations incorporated into the criterion function. Recovery of the true configuration and true distances for (i) single replication data with low error levels and (ii) matrix conditional data with high error levels was studied as a function of the type of error distribution, fitting criterion, and dimensionality. Results indicated that if the data conform to the error distribution hypotheses, then the corresponding fitting criteria provide improved recovery, but only for data with low error levels when the true dimensionality is known.

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Wolter, Ulrike Tina Barbara. "Multidimensional scaling analysis of coping strategies for chronic pain." Case Western Reserve University School of Graduate Studies / OhioLINK, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=case1054913618.

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14

Welch, Kathryn A. "The perceived dimensions of jobs: a multidimensional scaling approach." Diss., Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/54806.

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Recent research has revealed ambiguous evidence for the validity of the cognitive complexity (CC) construct. Some authors (Bieri, Atkins, Briar, Leaman, Miller, & Tripodi, 1966; Scott, Osgood, & Peterson, 1979) have suggested that a potentially useful method for examining CC is multidimensional scaling. The present study examined such an operational definition. The present study also examined the perceptual dimensions that underlie individuals' perceptions of jobs. Three hundred and five subjects rated the similarity of pairs of job titles, completed the Role Construct Repertory Test (REP), and later rated videotaped vignettes in a performance appraisal simulation. Multidimensional scaling extracted the subjects’ dimensionality. Due to an unstable solution, the study’s first three hypotheses (that dimensionality would predict rater accuracy, that dimensionality would predict rater accuracy better than the traditional Role Construct Repertory test, and that dimensionality and the REP would be correlated) were untestable. Multidimensional scaling was not a useful approach in this context. The fourth hypothesis stated that the present data would replicate the three-dimensional job characteristics model of Stone and Gueutal (1985). Results indicated that the Stone and Gueutal configuration was not supported. Thus, job design efforts predicated on their model appear premature.
Ph. D.

15

Yon, Haniza. "Multidimensional item response theory (MIRT) approaches to vertical scaling." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (Ph. D.)--Michigan State University. Measurement and Quantitative Methods , 2006.
Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p. 113-119). Also issued in print.

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Timm, Andrew. "An investigation of Multidimensional Scaling with an emphasis on the development of an R based Graphical User Interface for performing Multidimensional Scaling procedures." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/6880.

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This dissertation is centered around the development of a graphical user interface, using the R statistical programing language, for performing Multidimensional Scaling. This program is called the MDS-GUI. Multidimensional Scaling (MDS) is one of the groups of multivariate analysis techniques that is used for dimension reduction. In general, these methods of MDS can be viewed as the problem of constructing a map when given a set of interpoint distances. The graphical configuration is produced, usually in two or three dimensions, in such a way that objects of the data are represented by points, where the Euclidean distances between them optimally represents the given set of observed distances. The MDS-GUI was developed using a combination of R and the scripting language tcltk. The primary objective of its design was to provide a comprehensive range of MDS methods and analytical tools that are accessed in a point and click manner. The target user group of the software is therefore widely spread as no coding and only a little expertise on MDS is required for its use. The capabilities of the MDS-GUI are demonstrated with the use of three data sets. The first is the well known and well used Morse-Code data; the second is a synthetic microarray based data set; and the third concerns the nutritional contents of a group of the cereals from the Kellog's company. The program will be the first complete MDS based GUI for the R-Environment, and will also be the package that provides access to the widest range of MDS methods in R.

17

Jones, Patricia Ann Blodgett. "Assessment of dimensionality in dichotomously-scored data using multidimensional scaling." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/184267.

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The effectiveness of multidimensional scaling (MDS) techniques in recovering the underlying dimensionality of dichotomously-scored data was examined for unidimensional and multidimensional data. Thirty-three data sets of varying numbers of dimensions with differing patterns of item discrimination were generated using a multidimensional latent trait model in a Monte Carlo simulation study. Margin-sensitive measures (agreement, phi, and kappa) and margin-free measures (Φ/ Φ(max), Yule's Q, and the tetrachoric correlation) were used as measures of similarity and the resulting matrices were scaled in one through five dimensions. Values of the stress coefficient, S₁, S₁ by dimensionality plots, and plot configurations were examined to determine the dimensionality of the item set. Principal components analyses (PCAs) of phi and tetrachoric matrices were carried out as a basis for comparison. In addition, MDS and PCA were used to examine a data set comprised of items obtained from the routing tests of the Head Start Measures Battery. Two effects of item discrimination on MDS results were especially noteworthy. First, factors tended to be located equally distant from each other in the MDS space. Items were located closest to the factor for which the primary factor loading occurred. Second, as item discrimination decreased, items tended to be more widely dispersed from their appropriate locations in space. Extra dimensions in the MDS representational space were required for margin-sensitive coefficients to accommodate difficulty effects. Margin-free coefficients generally eliminated difficulty-related dimensions, although occasional problems were noted with the tetrachoric correlation. Analysis of the HSMB revealed that the data were primarily unidimensional, although specific effects due to each subtest were clearly present in the analysis. MDS was found to be a useful technique and its use in conjunction with PCA or factor analysis is recommended.

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Odondi, Maurice Jacob. "Multidimensional analysis of successive categories (rating) data by dual scaling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ28031.pdf.

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Molytė, Alma. "Investigation of Combinations of Vector Quantization Methods with Multidimensional Scaling." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110630_133937-99171.

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Often there is a need to establish and understand the structure of multidimensional data: their clusters, outliers, similarity and dissimilarity. One of solution ways is a dimensionality reduction and visualization of the data. If a huge datasets is analyzed, it is purposeful to reduce the number of the data items before visualization. The area of research is reduction of the number of the data analyzed and mapping the data in a plane. In the dissertation, vector quantization methods, based on artificial neural networks, and visualization methods, based on a dimensionality reduction, have been investigated. The consecutive and integrated combinations of neural gas and multidimensional scaling have been proposed here as an alternative to combinations of self-organizing maps and multidimensional scaling. The visualization quality is estimated by König’s topology preservation measure, Spearman’s rho and MDS error. The measures allow us to evaluate the similarity preservation quantitatively after a transformation of multidimensional data into a lower dimension space. The ways of selecting the initial values of two-dimensional vectors in the consecutive combination and the first training block of the integrated combination have been proposed and the ways of assigning the initial values of two-dimensional vectors in all the training blocks, except the first one, of the integrated combination have been developed. The dependence of the quantization error on the values of training... [to full text]
Dažnai iškyla būtinybė nustatyti ir giliau pažinti daugiamačių duomenų struktūrą: susidariusius klasterius, itin išsiskiriančius objektus, objektų tarpusavio panašumą ir skirtingumą. Vienas iš sprendimų būdų – duomenų dimensijos mažinimas ir jų vizualizavimas. Kai analizuojamos didelės duomenų aibės, tikslinga prieš vizualizavimą sumažinti ne tik dimensiją, bet ir duomenų skaičių. Šio darbo tyrimų sritis yra daugiamačių duomenų skaičiaus mažinimas ir duomenų atvaizdavimas plokštumoje. Disertacijoje nagrinėjami dirbtiniais neuroniniais tinklais grindžiami vektorių kvantavimo ir dimensijos mažinimu pagrįsti vizualizavimo metodai. Kaip alternatyva saviorganizuojančių neuroninių tinklų ir daugiamačių skalių junginiams, darbe pasiūlyti nuoseklus neuroninių dujų ir daugiamačių skalių junginys bei integruotas, atsižvelgiantis į neuroninių dujų metodo mokymosi eigą ir leidžiantis gauti tikslesnę daugiamačių vektorių projekciją plokštumoje. Junginiais gautų vaizdų kokybės vertinimui pasirinkti Konigo matas, Spirmano koeficientas bei MDS paklaida. Šie matai leidžia kiekybiškai įvertinti panašumų išlaikymą po daugiamačių duomenų transformavimo į mažesnės dimensijos erdvę. Taip pat pasiūlyti dvimačių vektorių pradinių koordinačių parinkimo būdai nuosekliame junginyje ir integruoto junginio pirmame mokymo bloke bei koordinačių reikšmių priskyrimo būdai integruoto junginio kituose mokymo blokuose. Eksperimentiškai nustatyta kvantavimo paklaidos priklausomybė nuo neuroninių dujų tinklo... [toliau žr. visą tekstą]

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Riley, Anthony David. "Probing chromosome structure using multidimensional scaling of DNA contact matrices." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/7262/.

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Chromosome conformation capture technology has provided a route to studying genome structure through DNA-DNA contact-counts. An iteration of chromosome conformation capture technology is Hi-C, which provides genome wide two dimensional contact-count data. The contact-count data from Hi-C can be viewed as a proxy for distance and using some transform function can be transformed into estimated distances. These estimated distances can be fitted into Euclidean space using the statistical tools of multidimensional scaling to give estimated chromosome or genome configurations. The first part of this thesis takes the Hi-C contact-count data for Chromosome 14, transforms it into estimated distances which are fitted into Euclidean space to give an estimated chromosome configuration. Steps are also taken to pre-process the genome contact-count matrix to refine the information held within it. The pre-processed genome contact-count matrix is transformed into estimated distances, which are fitted into Euclidean space to give an estimated genome configuration. The estimated chromosome and genome configurations are investigated, to find if known features of these structures are captured through fitting the Hi-C data. The second part of this thesis simulates contact-count data from simple configurations. Using the inverse of the transform functions the distances between points in a configuration can be transformed into mean contact-counts. The mean contact-counts are perturbed using a suitable distribution function to provide perturbed contact-counts, which are transformed into perturbed distances. The perturbed distances can be fitted into Euclidean space to give a fitted configurations. The properties of the fitted configurations are investigated and compared with the original configurations, and the properties of the perturbed distances are also investigated. Then steps are taken to improve the fitted configurations using information from the properties of the perturbed distances, with the successful techniques applied to estimating the chromosome configuration.

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Esparza, Oscar Armando. "Development of a multidimensional fatalism measure." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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22

Ahmed, Ahmed Abdol-Monem. "Experimental study of localization in sensor networks and design of adaptive localization." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/5829.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (Novembr 15, 2006) Vita. Includes bibliographical references.

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Roach, John O. "Multidimensional scaling of political differences : a study of belief-disbelief systems and ad hoc theories of political psychology around the proposed strategic defense initiative." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/30963.

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24

Broughton, Ross Harold. "Distance from the protype : a multidimensional scaling approach to personality assessment." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26777.

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An MDS (MultiDimenaional Scaling) model of personality assessment is presented as an alternative method of personality assessment designed to incorporate recently discovered cognitive principles relating to how people mentally organize categories (Roach, 1978), including personality trait categories (Broughton, 1984). The MDS model is shown to generalize from a tool for evaluating the self concept and the semantics of interpersonal categories (Partridge, 1984) to one that taps the full gamut of personality assessment in the interpersonal domain as defined by Wiggins (1979). In this paradigm, subjects rated the similarity of their own personality to prototypical characters described in short stories, or vignettes. In asking subjects to compare themselves to hypothetical people who display prototypical behaviors (based on the act-frequency prototype analyses of Buss & Craik, 1980), one is able to standardize the measure against which similarity ratings are made. Thus "prototypical dominance" (in this case operationalized as an excellent example of what it means in behavioral terms to be dominant) is the same for each subject, as is prototypical extraversion, aggression, and so on. Unlike traditional self-report measures, the respondent is not required to provide his or her own (possibly idiosyncratic) trait definition. Study 1 involved the development and use of the vignette materials in a paper and pencil administration. In this study 25 undergraduate subjects rated the 28 nonredundant pairs of eight vignette characters for similarity. The usefulness of the vignettes as personality testing stimuli was gauged according to structural criteria, namely the circular ordering (circumplexity) to emerge from the MDS analysis of the eight vignette stimuli. Although the results were judged satisfactory, steps were taken in Study 2 to improve the interpersonal meaning of two of the prototype stories, in an attempt to improve the solution. In Study 3, a microcomputer administration involving 158 participants, subjects compared the eight stimuli for similarity as in Study 1 but also compared themselves (each subject's usual and ideal self) to the eight vignette characters. Derived MDS distances from the prototypical characters were compared with conventional self-report scale scores from four widely used personality inventories (the Personality Research Form, PRF; Adjective Check List, ACL; California Psychological Inventory, CPI; and the Interpersonal Adjective Scales, IAS). Correlational analyses revealed low to moderate congruence between the MDS technique and these traditional personality measures. Three multiple regression analyses were performed to test how well subjects' MDS distance scores could predict standard trait measures. Results from the first analysis indicated that MDS measures were better at predicting IAS self-esteem than the four traditional Inventories. The second analysis showed that MDS distance scores better predicted IAS dominance than the remaining three inventories. The purpose of the third regression analysis was to test the comparative predictive validity of the MDS scores with a different self-report method PRF dominance. The MDS measures placed last in this category, but were not far behind the others.
Arts, Faculty of
Psychology, Department of
Graduate

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Klimenta, Mirza [Verfasser]. "Extending the Usability of Multidimensional Scaling for Graph Drawing / Mirza Klimenta." Konstanz : Bibliothek der Universität Konstanz, 2012. http://d-nb.info/1030479127/34.

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26

Welchew, David Edward. "Multidimensional scaling of human brain connectivity in functional magnetic resonance imaging." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615148.

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Rusch, Thomas, Patrick Mair, and Kurt Hornik. "COPS Cluster Optimized Proximity Scaling." WU Vienna University of Economics and Business, 2015. http://epub.wu.ac.at/4465/1/COPS.pdf.

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Proximity scaling (i.e., multidimensional scaling and related methods) is a versatile statistical method whose general idea is to reduce the multivariate complexity in a data set by employing suitable proximities between the data points and finding low-dimensional configurations where the fitted distances optimally approximate these proximities. The ultimate goal, however, is often not only to find the optimal configuration but to infer statements about the similarity of objects in the high-dimensional space based on the the similarity in the configuration. Since these two goals are somewhat at odds it can happen that the resulting optimal configuration makes inferring similarities rather difficult. In that case the solution lacks "clusteredness" in the configuration (which we call "c-clusteredness"). We present a version of proximity scaling, coined cluster optimized proximity scaling (COPS), which solves the conundrum by introducing a more clustered appearance into the configuration while adhering to the general idea of multidimensional scaling. In COPS, an arbitrary MDS loss function is parametrized by monotonic transformations and combined with an index that quantifies the c-clusteredness of the solution. This index, the OPTICS cordillera, has intuitively appealing properties with respect to measuring c-clusteredness. This combination of MDS loss and index is called "cluster optimized loss" (coploss) and is minimized to push any configuration towards a more clustered appearance. The effect of the method will be illustrated with various examples: Assessing similarities of countries based on the history of banking crises in the last 200 years, scaling Californian counties with respect to the projected effects of climate change and their social vulnerability, and preprocessing a data set of hand written digits for subsequent classification by nonlinear dimension reduction. (authors' abstract)
Series: Discussion Paper Series / Center for Empirical Research Methods

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Chuang, Ming-Chuen. "The application of multidimensional scaling to a robotic vision model of space perception /." Thesis, Connect to Dissertations & Theses @ Tufts University, 1988.

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Thesis (Ph.D.)--Tufts University, 1988.
Submitted to the Dept. of Engineering Design. Includes bibliographical references. Access restricted to members of the Tufts University community. Also available via the World Wide Web;

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BROOKS, David, and d. brooks@ecu edu au. "Public street surveillance: a psychometric study on the perceived social risk." Edith Cowan University. Computing, Health And Science: School Of Engineering, 2003. http://adt.ecu.edu.au/adt-public/adt-ECU2006.0007.html.

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Public street surveillance, a domain of Closed Circuit Television (CCTV), has grown enormously and is becoming common place with increasing utilization in society as an all-purpose security tool. Previous authors (Ditton, 1999; Davies, 1998; Horne, 1998; Tomkins, 1998) have raised concern over social, civil and privacy issues, but there has been limited research to quantify these concerns. There are a number of core aspects that could relocate the risk perception and therefore, social support of public street surveillance. This study utilized the psychometric paradigm to quantitatively measure the social risk perception of public street surveillance. The psychometric paradigm is a method that presents risk perception in a two factor representation, being dread risk and familiarity to risk. Four additional control activities and technologies were tested, being radioactive waste, drinking water chlorination, coal mining disease and home swimming pools. Analysis included spatial representation, and multidimensional scaling (MDS) Euclidean and INDSCAL methods. The study utilized a seven point Likert scale, pre and post methodology, and had a target population of N=2106, with a sample of N=135 (alpha=0.7).

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Kodali, Lata. "Extensions of Weighted Multidimensional Scaling with Statistics for Data Visualization and Process Monitoring." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99911.

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This dissertation is the compilation of two major innovations that rely on a common technique known as multidimensional scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data and creates low-dimensional versions. Project 1: Visualizations are useful when learning from high-dimensional data. However, visualizations, just as any data summary, can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. We incorporate uncertainty into visualizations created by a weighted version of MDS called WMDS. Uncertainty exists in these visualizations on the variable weights, the coordinates of the display, and the fit of WMDS. We quantify these uncertainties using Bayesian models in a method we call Informative Probabilistic WMDS (IP-WMDS). Visually, we display estimated uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in WMDS. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. Project 2: Analysis of network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated historical data unchanging over time. However, most networks result from temporally-evolving systems that exhibit intrinsic dynamic behavior. Monitoring such temporally-varying networks to detect anomalous changes has applications in both social and physical sciences. In this work, we simulate data from models that rely on MDS, and we perform an evaluation study of the use of summary statistics for anomaly detection by incorporating principles from statistical process monitoring. In contrast to most previous studies, we deliberately incorporate temporal auto-correlation in our study. Other considerations in our comprehensive assessment include types and duration of anomaly, model type, and sparsity in temporally-evolving networks. We conclude that the use of summary statistics can be valuable tools for network monitoring and often perform better than more involved techniques.
Doctor of Philosophy
In this work, two main ideas in data visualization and anomaly detection in dynamic networks are further explored. For both ideas, a connecting theme is extensions of a method called Multidimensional Scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data (all $p$ dimensions) and creates a low-dimensional projection of the data. That is, relationships in a dataset with presumably a large number of dimensions or variables can be summarized into a lower number of, e.g., two, dimensions. For a given data, an analyst could use a scatterplot to observe the relationship between 2 variables initially. Then, by coloring points, changing the size of the points, or using different shapes for the points, perhaps another 3 to 4 more variables (in total around 7 variables) may be shown in the scatterplot. An advantage of MDS (or any dimension-reduction technique) is that relationships among the data can be viewed easily in a scatterplot regardless of the number of variables in the data. The interpretation of any MDS plot is that observations that are close together are relatively more similar than observations that are farther apart, i.e., proximity in the scatterplot indicates relative similarity. In the first project, we use a weighted version of MDS called Weighted Multidimensional Scaling (WMDS) where weights, which indicate a sense of importance, are placed on the variables of the data. The problem with any WMDS plot is that inaccuracies of the method are not included in the plot. For example, is an observation that appears to be an outlier, really an outlier? An analyst cannot confirm this without further context. Thus, we created a model to calculate, visualize, and interpret such inaccuracy or uncertainty in WMDS plots. Such modeling efforts help analysts facilitate exploratory data analysis. In the second project, the theme of MDS is extended to an application with dynamic networks. Dynamic networks are multiple snapshots of pairwise interactions (represented as edges) among a set of nodes (observations). Over time, changes may appear in some of the snapshots. We aim to detect such changes using a process monitoring approach on dynamic networks. Statistical monitoring approaches determine thresholds for in-control or expected behavior that are calculated from data with no signal. Then, the in-control thresholds are used to monitor newly collected data. We applied this approach on dynamic network data, and we utilized a detailed simulation study to better understand the performance of such monitoring. For the simulation study, data are generated from dynamic network models that use MDS. We found that monitoring summary statistics of the network were quite effective on data generated from these models. Thus, simple tools may be used as a first step to anomaly detection in dynamic networks.

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Spencer, Steven Gerry. "The Strength of Multidimensional Item Response Theory in Exploring Construct Space that is Multidimensional and Correlated." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd646.pdf.

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Kapoor, Kunal. "A multidimensional scaling and participatiory design approach to classify open ended aircraft maintenance data." Connect to this title online, 2006. http://etd.lib.clemson.edu/documents/1175186103/.

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Bennett, Stephen John. "Curved axes and trajectories for multidimensional scaling, with applications to sensory and consumer data." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494083.

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The analysis of sensory and consumer-derived data involves the use of many different statistical techniques. The vast majority of these are multivariate in for example, multidimensional scaling (MDS) and biplots. However, univariate techniques such as repeated measures analysis of variance and the Bradley-Terry model for paired comparison data are also common. This thesis introduces enhancements to MDS based on the use of curved axes and trajectories.

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Mair, Patrick, Thomas Rusch, and Kurt Hornik. "The grand old party - a party of values?" Springer, 2014. http://dx.doi.org/10.1186/2193-1801-3-697.

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In this article we explore the semantic space spanned by self-reported statements of Republican voters. Our semantic structure analysis uses multidimensional scaling and social network analysis to extract, explore, and visualize word patterns and word associations in response to the stimulus statement "I'm a Republican, because ..." which were collected from the official website of the Republican Party. With psychological value theory as our backdrop, we examine the association of specific keywords within and across the statements, compute clusters of statements based on these associations, and explore common word sequences Republican voters use to characterize their political association with the Party. (authors' abstract)

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Farruggia, Frank Thomas. "A FLORISTIC DESCRIPTION OF A NEOTROPICAL COASTAL SAVANNA IN BELIZE." Miami University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=miami1090942598.

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Deville, Craig W. "An Investigation of Content- and Construct-Related Evidence of Self-Assessment Items Using Multidimensional Scaling." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1389191999.

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Deville, Craig W. "An investigation of content- and construct-related evidence of self-assessment items using multidimensional scaling /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487848891513513.

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Potter, Timothy. "The cognitive representation of face distinctiveness : theoretical contribution and direct evidence for face space models." Université catholique de Louvain, 2008. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-09082008-092444/.

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The distinctiveness of a face is a crucial factor for its ability to be discriminated, memorized, and identified correctly. In this thesis, we provided a contribution to face distinctiveness by examining the impact of socially relevant factors such as attractiveness, group and emotional expression on distinctiveness. We show that attractive faces are more similar to each other than unattractive faces, using female Caucasian faces and male faces of a Caucasian and heterogeneous Non-Caucasian group. We also show, using 3D face generated faces of photo-realistic quality, that attractive faces were closer to the prototype of only their specific group, and that hence typicality of attractive faces was group specific. Lastly, we show that assigning an emotional expression to a face that is evaluatively incongruent with its race makes it more psychologically distinctive, as revealed in perceptual discrimination and memory tasks.

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Li, Wang-on, and 李允安. "The assessment of learning outcome: knowledgestructure." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31227582.

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Kandathil, Shaun. "On conformational sampling in fragment-based protein structure prediction." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/on-conformational-sampling-in-fragmentbased-protein-structure-prediction(15b113b0-c389-4e8e-ae7c-d399e8a72b03).html.

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Fragment assembly methods represent the state of the art in computational protein structure prediction. However, one limitation of these methods, particularly for larger protein structures, is inadequate conformational sampling. This thesis describes studies aimed at uncovering potential causes of ineffective sampling, and the development of methods to try and address these problems. To identify behaviours that might lead to poor conformational sampling, we developed measures to study fragment-based sampling trajectories. Applying these measures to the Rosetta Abinitio and EdaFold methods showed similarities and differences in the ways that these methods make predictions, and pointed to common limitations. In both protocols, structural features such as alpha-helices were more frequently altered during the search, as compared with regions such as loops. Analyses of the fragment libraries used by these methods showed that fragments covering loop regions were less likely to possess native-like structural features, and this likely exacerbated the problems of inadequate sampling in these regions. Inadequate loop sampling leads to poor fold-level exploration within individual runs of methods such as Rosetta, and this necessitates the use of many independent runs. Guided by these findings, we developed new heuristic-based search algorithms. These algorithms were designed to facilitate the exploration of multiple energy basins within runs. Over many runs, the enhanced exploration in our protocols produced decoy sets with larger fractions of native-like solutions as compared to runs of Rosetta. Experiments with different fragment sets indicated that our methods could better translate increased fragment set quality into improvements in predictive accuracy distributions. These improvements depend most strongly on the ability of search algorithms to reliably generate native-like structures using a fragment set. In contrast, inadequate retention of native-like decoys when associated with unfavourable score values appears to be less of an issue. This thesis shows that targeted developments in conformational sampling strategies can improve the accuracy and reliability of predictions. With effective conformational sampling methods, developments in methods for fragment set construction and other areas may more reliably enhance predictive ability.

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Ganey, Raeesa. "Biplots based on principal surfaces." Doctoral thesis, Faculty of Science, 2019. https://hdl.handle.net/11427/31695.

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Principal surfaces are smooth two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of the data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. Biplots are considered as extensions of the ordinary scatterplot by providing for more than three variables. When the difference between data points are measured using a Euclidean embeddable dissimilarity function, observations and the associated variables can be displayed on a nonlinear biplot. A nonlinear biplot is predictive if information on variables is added in such a way that it allows the values of the variables to be estimated for points in the biplot. Prediction trajectories, which tend to be nonlinear are created on the biplot to allow information about variables to be estimated. The goal is to extend the idea of nonlinear biplot methodology onto principal surfaces. The ultimate emphasis is on high dimensional data where the nonlinear biplot based on a principal surface allows for visualisation of samples, variable trajectories and predictive sets of contour lines. The proposed biplot provides more accurate predictions, with an additional feature of visualising the extent of nonlinearity that exists in the data.

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Choe, Byongho. "Nonmetric multidimensional scaling of complex sounds dimensions of preference ratings and perceived similarity of vehicle noises /." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962077100.

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Neoh, Jun. "Application of meta-analysis, multidimensional scaling, structural equation modelling, and multilevel modelling in analysing carpooling behaviour." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/395306/.

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Dwyer, Theodore James. "An Assessment of Paired Similarities and Card Sorting." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000158.

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Kim, Young Yee. "Effects of test linking methods on proficiency classification UIRT versus MIRT linking /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (Ph.D.)--Michigan State University. Measurement and Quantiative Methods Educational Policy, 2008.
Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p.193-199). Also issued in print.

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Llerena, Soledad Espezúa. "Mapeamento de dados genômicos usando escalonamento multidimensional." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-30072008-094122/.

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Neste trabalho são exploradas diversas técnicas de escalonamento multidimensional (MDS), com o objetivo de estudar sua aplicabilidade no mapeamento de dados genômicos resultantes da técnica RFLP-PCR, sendo esse mapeamento realizado em espaços de baixa dimensionalidade (2D ou 3D) com o fim de aproveitar a habilidade de análise e interpretação visual que possuem os seres humanos. Foi realizada uma análise comparativa de diversos algoritmos MDS, visando sua aptidão para mapear dados genômicos. Esta análise compreendeu o estudo de alguns índices de desempenho como a precisão no mapeamento, o custo computacional e a capacidade de induzir bons agrupamentos. Para a realização dessa análise foi desenvolvida a ferramenta \"MDSExplorer\", a qual integra os algoritmos estudados e várias opções que permitem comparar os algoritmos e visualizar os mapeamentos. Á análise realizada sobre diversos bancos de dados citados na literatura, sugerem que o algoritmo LANDMARK possui o menor tempo computacional, uma precisão de mapeamento similar aos demais algoritmos, e uma boa capacidade de manter as estruturas existentes nos dados. Finalmente, o MDSExplorer foi usado para mapear um banco de dados genômicos: o banco de estirpes de bactérias fixadoras de nitrogênio, pertencentes ao gênero Bradyrhizobium, com objetivo de ajudar o especialista a inferir visualmente alguma taxonomia nessas estirpes. Os resultados na redução dimensional desse banco de dados sugeriram que a informação relevante (acima dos 60% da variância acumulada) para as regiões 16S, 23S e IGS estaria nas primeiras 5, 4 e 9 dimensões respectivamente.
In this work were studied various Multidimensional Scaling (MDS) techniques intended to apply in the mapping of genomics data obtained of RFLP-PCR technique. This mapping is done in a low dimensional space (2D or 3D), and has the intention of exploiting the visual human capability on analysis and synthesis. A comparative analysis of diverse algorithms MDS was carried out in order to devise its ubiquity in representing genomics data. This analysis covers the study of some indices of performance such as: the precision in the mapping, the computational cost and the capacity to induce good groupings. The purpose of this analysis was developed a software tool called \"MDSExplorer\", which integrates various MDS algorithms and some options that allow to compare the algorithms and to visualize the mappings. The analysis, carried out over diverse datasets cited in the literature, suggest that the algorithm LANDMARK has the lowest computational time, a good precision in the mapping, and a tendency to maintain the existing structures in the data. Finally, MDSExplorer was used to mapping a real genomics dataset: the RFLP-PRC images of a Brazilian collection of bacterial strains belonging to the genus Bradyrhizobium (known by their capability to transform the nitrogen of the atmosphere into compounds useful for the host plants), with the objective to aid the specialist to infer visually a taxonomy in these strains. The results in reduction of dimensionality in this data base, suggest that the relevant information (above 60% of variance accumulated) to the region 16S, 23S and IGS is around 5, 4 and 9 dimensions respectively.

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Vincent, David E. "PORTABLE INDOOR MULTI-USER POSITION TRACKING SYSTEM FOR IMMERSIVE VIRTUAL ENVIRONMENTS USING SENSOR FUSION WITH MULTIDIMENSIONAL SCALING." Miami University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=miami1335634621.

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Arvidsson, Martin. "Getting a Feel for Tactile Space : Exploring Haptic Perception of Microtexture." Doctoral thesis, Stockholms universitet, Psykologiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-82593.

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The present thesis is based on three studies that research different aspects of fine texture perception. The goal is to better understand the mechanisms involved in haptic perception of textures below 200 µm, also known as microtextures. Study I was conducted to establish a friction measurement model and relating the friction measurements to perceived coarseness of fine textures. A set of printing papers was used as stimulus material. In Study II an expanded set, including the set of Study I, was used as stimuli in a multidimensional scaling (MDS) experiment of haptic fine texture perception. Through scaling of perceptual attributes and similarities, a three dimensional space was found to best describe the data and the dimensions were interpreted as rough-smooth, thick-thin and distinct-indistinct. In Study III a series of model surfaces were manufactured with a systematically varied sinusoidal pattern, spanning from 300 nm to 80 µm. As in Study II, a similarity experiment was conducted and a two dimensional space was chosen, the dimensions of which were explained well through friction and the wavelength. Together these three studies form a better picture of fine texture perception. The dimensionality found with paper stimuli was very similar to the corresponding spaces for marcrotextures of everyday materials, even though a different perceptual system is used for fine texture perception. Regardless if the information is coded through the spatial or the vibratory sense, the perception does not seem to differ in dimensionality. Further, the largest among the microtextures seem to have been perceived as carrying spatial information. On the systematically varied, rigid, textures, the MDS space did not come out in a similar fashion to those of everyday materials but instead similar to the physical properties that characterizes the change in the textures. It was further found that the participants in Study III successfully discriminated textures with an amplitude of 13 nm from the unwrinkled surfaces. From these studies the main conclusions are (a) haptically measured friction and surface roughness are important contributors to fine texture perception, (b) even at microscales, spatial information is retrieved haptically, probably through vibrations, and (c) persons can haptically discriminate textures at a nanoscale.

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YONEDA, Hiroyuki, Ioki HARA, Takeshi FURUHASHI, Tomohiro YOSHIKAWA, Toshikazu FUKAMI, 洋之米田, 以起原, 武. 古橋, 大弘吉川, and 俊和深見. "可視空間上でのインタラクティブクラスタリングによるマイノリティ発見に関する検討." 日本感性工学会, 2009. http://hdl.handle.net/2237/20849.

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McNelis, Kathleen. "The underlying dimensionality of people's implicit job theories across cognitive sets : implications for comparable worth /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487262513406512.

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Dissertations / Theses on the topic 'Multidimensional scaling'

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