Bibliografías temáticas / Multidimensional scaling

Literatura académica sobre el tema "Multidimensional scaling"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 7 de septiembre de 2023

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Artículos de revistas sobre el tema "Multidimensional scaling"

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Gower, J. C., F. Cox y M. A. A. Cox. "Multidimensional Scaling." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, n.º 1 (1996): 184. http://dx.doi.org/10.2307/2983485.

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Jeffers, J. N. R. y Mark L. Davison. "Multidimensional Scaling." Statistician 34, n.º 2 (1985): 257. http://dx.doi.org/10.2307/2988171.

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Jolliffe, Ian. "Multidimensional Scaling". Technometrics 38, n.º 4 (noviembre de 1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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Mugavin, Marie E. "Multidimensional Scaling". Nursing Research 57, n.º 1 (enero de 2008): 64–68. http://dx.doi.org/10.1097/01.nnr.0000280659.88760.7c.

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Hout, Michael C., Megan H. Papesh y Stephen D. Goldinger. "Multidimensional scaling". Wiley Interdisciplinary Reviews: Cognitive Science 4, n.º 1 (8 de octubre de 2012): 93–103. http://dx.doi.org/10.1002/wcs.1203.

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Aflalo, Y. y R. Kimmel. "Spectral multidimensional scaling". Proceedings of the National Academy of Sciences 110, n.º 45 (9 de octubre de 2013): 18052–57. http://dx.doi.org/10.1073/pnas.1308708110.

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Venna, Jarkko y Samuel Kaski. "Local multidimensional scaling". Neural Networks 19, n.º 6-7 (julio de 2006): 889–99. http://dx.doi.org/10.1016/j.neunet.2006.05.014.

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Spence, Ian y Stephan Lewandowsky. "Robust multidimensional scaling". Psychometrika 54, n.º 3 (septiembre de 1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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de Leeuw, Jan y Patrick J. F. Groenen. "Inverse Multidimensional Scaling". Journal of Classification 14, n.º 1 (1 de enero de 1997): 3–21. http://dx.doi.org/10.1007/s003579900001.

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Rajawat, Ketan y Sandeep Kumar. "Stochastic Multidimensional Scaling". IEEE Transactions on Signal and Information Processing over Networks 3, n.º 2 (junio de 2017): 360–75. http://dx.doi.org/10.1109/tsipn.2017.2668145.

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Tesis sobre el tema "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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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.
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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 y 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.
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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.
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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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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 y 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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Libros sobre el tema "Multidimensional scaling"

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A, Cox Michael A., ed. Multidimensional scaling. 2a ed. Boca Raton: Chapman & Hall/CRC, 2001.

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Davison, Mark L. Multidimensional scaling. Malabar, Fla: Krieger Pub. Co., 1992.

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Borg, Ingwer, Patrick J. F. Groenen y Patrick Mair. Applied Multidimensional Scaling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31848-1.

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Borg, Ingwer y Patrick Groenen. Modern Multidimensional Scaling. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2711-1.

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Borg, Ingwer. Applied Multidimensional Scaling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Biela, Adam. Skalowanie wielowymiarowe jako metoda badań naukowych. Lublin: Tow. Naukowe Katolickiego Uniwersytetu Lubelskiego, 1992.

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Chino, Naohito, Shingo Saburi y Kensuke Okada. Hitaishō MDS no riron to ōyō. 8a ed. Kyōto-shi: Gendai Sūgakusha, 2012.

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Schubert, Leo. Lösungsansätze der mehrdimensionalen Skalierung mit Berücksichtigung unterschiedlicher Datenniveaus. Königstein/Ts: A. Hain, 1985.

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Nannestad, Peter. Geometriske repræsentationer af relationelle datastrukturer =: Geometrische Repräsentationen relationeller Datenstrukturen : studier i multidimensional skalering. Århus: Politica, 1985.

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Kleiss, James A. Terrain visual cue analysis for simulating low-level flight: A multidimensional scaling approach. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1990.

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Capítulos de libros sobre el tema "Multidimensional scaling"

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Härdle, Wolfgang Karl y Zdeněk Hlávka. "Multidimensional Scaling". En Multivariate Statistics, 289–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_17.

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Härdle, Wolfgang y Léopold Simar. "Multidimensional Scaling". En Applied Multivariate Statistical Analysis, 373–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_15.

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Everitt, Brian S. y Graham Dunn. "Multidimensional Scaling". En Applied Multivariate Data Analysis, 93–124. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch5.

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Shen, Heng Tao. "Multidimensional Scaling". En Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_548-2.

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du Toit, S. H. C., A. G. W. Steyn y R. H. Stumpf. "Multidimensional Scaling". En Springer Texts in Statistics, 105–75. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4950-4_6.

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Härdle, Wolfgang Karl y Léopold Simar. "Multidimensional Scaling". En Applied Multivariate Statistical Analysis, 455–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_17.

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Mukherjee, S. P., Bikas K. Sinha y Asis Kumar Chattopadhyay. "Multidimensional Scaling". En Statistical Methods in Social Science Research, 113–22. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2146-7_11.

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Adachi, Kohei. "Multidimensional Scaling". En Matrix-Based Introduction to Multivariate Data Analysis, 247–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_16.

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Härdle, Wolfgang Karl y Léopold Simar. "Multidimensional Scaling". En Applied Multivariate Statistical Analysis, 397–412. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_16.

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Jacoby, William G. y David J. Ciuk. "Multidimensional Scaling". En The Wiley Handbook of Psychometric Testing, 375–412. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118489772.ch14.

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Actas de conferencias sobre el tema "Multidimensional scaling"

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Mackute-Varoneckiene, Ausra, Antanas Zilinskas y Audrius Varoneckas. "Multidimensional scaling". En the International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1731740.1731805.

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Runkler, Thomas A. y James C. Bezdek. "Multidimensional scaling with multiswarming". En 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900225.

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Zhang, Xi, Hao Huang, Klaus Mueller y Shinjae Yoo. "Streaming Classical Multidimensional Scaling". En 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538942.

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Kumar, Sandeep y Ketan Rajawat. "Velocity-assisted multidimensional scaling". En 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015. http://dx.doi.org/10.1109/spawc.2015.7227102.

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Di Franco, Carmelo, Enrico Bini, Mauro Marinoni y Giorgio C. Buttazzo. "Multidimensional scaling localization with anchors". En 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2017. http://dx.doi.org/10.1109/icarsc.2017.7964051.

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Vu, Viet Minh, Adrien Bibal y Benoit Frenay. "iPMDS: Interactive Probabilistic Multidimensional Scaling". En 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534425.

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Qu, Taiguo y Zixing Cai. "A fast multidimensional scaling algorithm". En 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015. http://dx.doi.org/10.1109/robio.2015.7419726.

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Rajan, Raj Thilak, Geert Leus y Alle-Jan van der Veen. "Relative velocity estimation using multidimensional scaling". En 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714023.

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Hsia, Chi-Chun, Kuo-Yuan Lee, Chih-Chieh Chuang y Yu-Hsien Chiu. "Multidimensional scaling for fast speaker clustering". En 2010 7th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2010. http://dx.doi.org/10.1109/iscslp.2010.5684888.

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Kennedy, A., Xiaojun Wang, Zhen Liu y Bin Liu. "Frequency scaling for multidimensional packet classifiers". En China-Ireland International Conference on Information and Communications Technologies (CIICT 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080829.

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Informes sobre el tema "Multidimensional scaling"

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Cvetkovski, Andrej y Mark Crovella. Multidimensional Scaling in the Poincare Disk. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2011. http://dx.doi.org/10.21236/ada585960.

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Oh, Man-Suk y Adrian E. Raftery. Bayesian Multidimensional Scaling and Choice of Dimension. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2000. http://dx.doi.org/10.21236/ada458817.

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Kearsley, Anthony J., Richard A. Tapia y Michael W. Trosset. The Solution of the Metric STRESS and SSTRESS Problems in Multidimensional Scaling Using Newton's Method. Fort Belvoir, VA: Defense Technical Information Center, enero de 1995. http://dx.doi.org/10.21236/ada445621.

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Koven, Charles, R. Knox, R. Fisher, Forrest Hoffman, Trevor Keenan, D. Lawrence, M. Longo y B. Sanderson. Modular hybrid modeling to increase efficiency, explore structural uncertainty, andallow multidimensional complexity scaling in land surface models. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769750.

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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner y Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, febrero de 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.
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