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Articles de revues sur le sujet « Multidimensional scaling »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 9 juin 2025

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1

Gower, J. C., F. Cox, and M. A. A. Cox. "Multidimensional Scaling." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 184. http://dx.doi.org/10.2307/2983485.

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2

Jeffers, J. N. R., and Mark L. Davison. "Multidimensional Scaling." Statistician 34, no. 2 (1985): 257. http://dx.doi.org/10.2307/2988171.

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3

Lee, In-Soon. "Multidimensional Scaling." Journal of Korean Medical Library Association 19, no. 1 (June 1992): 1–6. http://dx.doi.org/10.69528/jkmla.1992.19.1.1.

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4

Jolliffe, Ian. "Multidimensional Scaling." Technometrics 38, no. 4 (November 1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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Mugavin, Marie E. "Multidimensional Scaling." Nursing Research 57, no. 1 (January 2008): 64–68. http://dx.doi.org/10.1097/01.nnr.0000280659.88760.7c.

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Hout, Michael C., Megan H. Papesh, and Stephen D. Goldinger. "Multidimensional scaling." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 1 (October 8, 2012): 93–103. http://dx.doi.org/10.1002/wcs.1203.

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Aflalo, Y., and R. Kimmel. "Spectral multidimensional scaling." Proceedings of the National Academy of Sciences 110, no. 45 (October 9, 2013): 18052–57. http://dx.doi.org/10.1073/pnas.1308708110.

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8

Venna, Jarkko, and Samuel Kaski. "Local multidimensional scaling." Neural Networks 19, no. 6-7 (July 2006): 889–99. http://dx.doi.org/10.1016/j.neunet.2006.05.014.

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9

Spence, Ian, and Stephan Lewandowsky. "Robust multidimensional scaling." Psychometrika 54, no. 3 (September 1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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de Leeuw, Jan, and Patrick J. F. Groenen. "Inverse Multidimensional Scaling." Journal of Classification 14, no. 1 (January 1, 1997): 3–21. http://dx.doi.org/10.1007/s003579900001.

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11

Rajawat, Ketan, and Sandeep Kumar. "Stochastic Multidimensional Scaling." IEEE Transactions on Signal and Information Processing over Networks 3, no. 2 (June 2017): 360–75. http://dx.doi.org/10.1109/tsipn.2017.2668145.

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Hébert, Pierre-Alexandre, Marie-Hélène Masson, and Thierry Denœux. "Fuzzy multidimensional scaling." Computational Statistics & Data Analysis 51, no. 1 (November 2006): 335–59. http://dx.doi.org/10.1016/j.csda.2006.02.020.

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Bronstein, M. M., A. M. Bronstein, R. Kimmel, and I. Yavneh. "Multigrid multidimensional scaling." Numerical Linear Algebra with Applications 13, no. 2-3 (2006): 149–71. http://dx.doi.org/10.1002/nla.475.

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Walundungo, Gloria, Marline Paendong, and Tohap Manurung. "Penggunaan Analisis Multidimensional Scaling Untuk Mengetahui Kemiripan Rumah Makan Di Manado Town Square Berdasarkan Kerakteristik Pelanggan." d'CARTESIAN 3, no. 1 (March 30, 2014): 30. http://dx.doi.org/10.35799/dc.3.1.2014.3806.

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Abstract Multidimensional Scaling (MDS) is a technique that can be used in multiple variables to determine the position of other objects based on similarity assessment. The purpose of this study is to obtain a description of the restaurant and the customers know the similarities between the restaurants as object of research. The result of Multidimensional Scaling map shows that WarungPojok and Solaria restaurant have a same rate in taste of food category due to the distance is near each other. As for the restaurant Bakmi Naga, Ayam Penyet and Kawan Baru have relative position between each other which means the three restaurants unsimillarity. Keywords : Multidimensional Scaling, Mapping, Map Perception, Restaurant . Abstrak Multidimensional Scalling (MDS)merupakan salah satu teknik peubah ganda yang dapat digunakan untuk menentukan posisi suatu objek lainnya berdasarkan penilaian kemiripannya. Tujuan dari penelitian ini adalah untuk memperoleh deskripsi pelanggan terhadap rumah makan serta mengetahui kemiripan antara rumah makan yang dijadikan objek penelitian. Hasil dari peta analisis Multidimensional Scaling dapat dilihat bahwa rumah makan Warung Pojok dan Solaria memiliki tingkat kemiripan di cita rasa makanankarena jarak yang saling berdekatan. Sedangkan untuk rumah makan Bakmi Naga, Ayam Penyet dan Kawan Baru menempati posisi relatif saling berjauhan antar satu dengan yang lain yang berarti ketiga rumah makan ini tidak mempunyai kemiripan atau ketakmiripan. Kata kunci : Multidimensional Scaling, Pemetaan, Peta Persepsi, Rumah Makan

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15

Andrecut, M. "Molecular dynamics multidimensional scaling." Physics Letters A 373, no. 23-24 (May 2009): 2001–6. http://dx.doi.org/10.1016/j.physleta.2009.04.007.

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16

Bakker, Ryan, and Keith T. Poole. "Bayesian Metric Multidimensional Scaling." Political Analysis 21, no. 1 (2013): 125–40. http://dx.doi.org/10.1093/pan/mps039.

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In this article, we show how to apply Bayesian methods to noisy ratio scale distances for both the classical similarities problem as well as the unfolding problem. Bayesian methods produce essentially the same point estimates as the classical methods, but are superior in that they provide more accurate measures of uncertainty in the data. Identification is nontrivial for this class of problems because a configuration of points that reproduces the distances is identified only up to a choice of origin, angles of rotation, and sign flips on the dimensions. We prove that fixing the origin and rotation is sufficient to identify a configuration in the sense that the corresponding maxima/minima are inflection points with full-rank Hessians. However, an unavoidable result is multiple posterior distributions that are mirror images of one another. This poses a problem for Markov chain Monte Carlo (MCMC) methods. The approach we take is to find the optimal solution using standard optimizers. The configuration of points from the optimizers is then used to isolate a single Bayesian posterior that can then be easily analyzed with standard MCMC methods.

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Dzhafarov, Ehtibar N., and Hans Colonius. "Multidimensional Fechnerian Scaling: Basics." Journal of Mathematical Psychology 45, no. 5 (October 2001): 670–719. http://dx.doi.org/10.1006/jmps.2000.1341.

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18

Zhang, Zhidong, and Luis Garcia. "Examining Dimensionality and Validity of the Academic Integrity Survey Instrument." Journal of Education and Development 7, no. 1 (February 8, 2023): 46. http://dx.doi.org/10.20849/jed.v7i1.1326.

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Dimensional reduction is one of the methods to ensure the quality of a questionnaire. This study examined two methods to reduce the dimension of the questionnaire: multidimensional scaling (MDS) and exploratory factor analysis (EFA). The questionnaire, Awareness of Academic Dishonesty consists of 30 questions. Participants included 110 college students. Multidimensional scaling analysis reduced the multidimensions to essentially two dimensions. The exploratory factor analysis reduced the multidimensions to three dimensions. MDS allowed the researchers to evaluate the questionnaire items by looking at the similarities of these data points. EFA provided an alternative thought about the construct of the questionnaire.

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19

Zhou, Ri-Gui, Canyun Tan, and Ping Fan. "Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI." Modern Physics Letters B 31, no. 17 (June 14, 2017): 1750184. http://dx.doi.org/10.1142/s0217984917501846.

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Reviewing past researches on quantum image scaling, only 2D images are studied. And, in a quantum system, the processing speed increases exponentially since parallel computation can be realized with superposition state when compared with classical computer. Consequently, this paper proposes quantum multidimensional color image scaling based on nearest-neighbor interpolation for the first time. Firstly, flexible representation of quantum images (FRQI) is extended to multidimensional color model. Meantime, the nearest-neighbor interpolation is extended to multidimensional color image and cycle translation operation is designed to perform scaling up operation. Then, the circuits are designed for quantum multidimensional color image scaling, including scaling up and scaling down, based on the extension of FRQI. In addition, complexity analysis shows that the circuits in the paper have lower complexity. Examples and simulation experiments are given to elaborate the procedure of quantum multidimensional scaling.

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20

Dourado, António, Paulo Barbeiro, Edgar Ferreira, Luís Amaral, António Coelho, and Dora Nogueira. "VISBREAKER ANALYSIS BY MULTIDIMENSIONAL SCALING." IFAC Proceedings Volumes 40, no. 9 (2007): 356–61. http://dx.doi.org/10.3182/20070723-3-pl-2917.00058.

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21

Lee, Su-Gi, Yong-Seok Choi, and Bo-Hui Lee. "Visualizations of Asymmetric Multidimensional Scaling." Korean Journal of Applied Statistics 27, no. 4 (August 31, 2014): 619–27. http://dx.doi.org/10.5351/kjas.2014.27.4.619.

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22

Ueda, Tohru. "Sensitivity Analysis in Multidimensional Scaling." Behaviormetrika 16, no. 25 (January 1989): 35–47. http://dx.doi.org/10.2333/bhmk.16.25_35.

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23

Moinpour, Reza, Paul E. Green, Frank J. Carmone, and Scott M. Smith. "Multidimensional Scaling: Concepts and Applications." Journal of Marketing Research 28, no. 4 (November 1991): 504. http://dx.doi.org/10.2307/3172796.

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24

Cox, Trevor F., and Michael A. A. Cox. "Multidimensional scaling on a sphere." Communications in Statistics - Theory and Methods 20, no. 9 (January 1991): 2943–53. http://dx.doi.org/10.1080/03610929108830679.

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25

Huang, Yameng, and Zhouchen Lin. "Binary Multidimensional Scaling for Hashing." IEEE Transactions on Image Processing 27, no. 1 (January 2018): 406–18. http://dx.doi.org/10.1109/tip.2017.2759250.

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26

Buja, Andreas, Deborah F. Swayne, Michael L. Littman, Nathaniel Dean, Heike Hofmann, and Lisha Chen. "Data Visualization With Multidimensional Scaling." Journal of Computational and Graphical Statistics 17, no. 2 (June 2008): 444–72. http://dx.doi.org/10.1198/106186008x318440.

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27

Perales, E., F. J. Burgos, M. Vilaseca, V. Viqueira, and F. M. Martínez-Verdú. "Graininess characterization by multidimensional scaling." Journal of Modern Optics 66, no. 9 (March 19, 2019): 929–38. http://dx.doi.org/10.1080/09500340.2019.1589006.

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28

Saeed, Nasir, Haewoon Nam, Mian Imtiaz Ul Haq, and Dost Bhatti Muhammad Saqib. "A Survey on Multidimensional Scaling." ACM Computing Surveys 51, no. 3 (July 16, 2018): 1–25. http://dx.doi.org/10.1145/3178155.

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29

Cox, Trevor F., Michael A. A. Cox, and Joao A. Branco. "Multidimensional scaling for n-tuples." British Journal of Mathematical and Statistical Psychology 44, no. 1 (May 1991): 195–206. http://dx.doi.org/10.1111/j.2044-8317.1991.tb00955.x.

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30

Rohde, Douglas L. T. "Methods for Binary Multidimensional Scaling." Neural Computation 14, no. 5 (May 1, 2002): 1195–232. http://dx.doi.org/10.1162/089976602753633457.

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Multidimensional scaling (MDS) is the process of transforming a set of points in a high-dimensional space to a lower-dimensional one while preserving the relative distances between pairs of points. Although effective methods have been developed for solving a variety of MDS problems, they mainly depend on the vectors in the lower-dimensional space having real-valued components. For some applications, the training of neural networks in particular, it is preferable or necessary to obtain vectors in a discrete, binary space. Unfortunately, MDS into a low-dimensional discrete space appears to be a significantly harder problem than MDS into a continuous space. This article introduces and analyzes several methods for performing approximately optimized binary MDS.

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31

Forero, Pedro A., and Georgios B. Giannakis. "Sparsity-Exploiting Robust Multidimensional Scaling." IEEE Transactions on Signal Processing 60, no. 8 (August 2012): 4118–34. http://dx.doi.org/10.1109/tsp.2012.2197617.

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32

Sato-Ilic, Mika, and Peter Ilic. "On a Multidimensional Cluster Scaling." Procedia Computer Science 36 (2014): 278–84. http://dx.doi.org/10.1016/j.procs.2014.09.094.

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Sato-Ilic, Mika. "Probabilistic Metric Based Multidimensional Scaling." Procedia Computer Science 168 (2020): 65–72. http://dx.doi.org/10.1016/j.procs.2020.02.258.

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34

Le, Huiling, and Christopher G. Small. "Multidimensional scaling of simplex shapes." Pattern Recognition 32, no. 9 (September 1999): 1601–13. http://dx.doi.org/10.1016/s0031-3203(99)00023-0.

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35

Jackson, J. Edward. "Key Texts in Multidimensional Scaling." Technometrics 27, no. 1 (February 1985): 88. http://dx.doi.org/10.1080/00401706.1985.10488020.

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Marcussen, Carl. "Multidimensional scaling in tourism literature." Tourism Management Perspectives 12 (October 2014): 31–40. http://dx.doi.org/10.1016/j.tmp.2014.07.003.

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Gower, John C., and Roger F. Ngouenet. "Nonlinearity effects in multidimensional scaling." Journal of Multivariate Analysis 94, no. 2 (June 2005): 344–65. http://dx.doi.org/10.1016/j.jmva.2004.05.008.

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Bae, Seung-Hee, Judy Qiu, and Geoffrey Fox. "Adaptive Interpolation of Multidimensional Scaling." Procedia Computer Science 9 (2012): 393–402. http://dx.doi.org/10.1016/j.procs.2012.04.042.

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Cozzens, Margaret B., and Rochelle Leibowitz. "Multidimensional scaling and threshold graphs." Journal of Mathematical Psychology 31, no. 2 (June 1987): 179–91. http://dx.doi.org/10.1016/0022-2496(87)90014-9.

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Buja, Andreas, and Deborah F. Swayne. "Visualization Methodology for Multidimensional Scaling." Journal of Classification 19, no. 1 (January 1, 2002): 7–43. http://dx.doi.org/10.1007/s00357-001-0031-0.

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Cox, Mitchell A. A., and Trevor F. Cox. "Interpreting stress in multidimensional scaling." Journal of Statistical Computation and Simulation 37, no. 3-4 (December 1990): 211–23. http://dx.doi.org/10.1080/00949659008811305.

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42

Goodrum, Abby A. "Multidimensional scaling of video surrogates." Journal of the American Society for Information Science and Technology 52, no. 2 (2001): 174–82. http://dx.doi.org/10.1002/1097-4571(2000)9999:9999<::aid-asi1580>3.0.co;2-v.

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Fenton, Mark, and Philip Pearce. "Multidimensional scaling and tourism research." Annals of Tourism Research 15, no. 2 (January 1988): 236–54. http://dx.doi.org/10.1016/0160-7383(88)90085-0.

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Dzhafarov, Ehtibar N. "Multidimensional Fechnerian Scaling: Perceptual Separability." Journal of Mathematical Psychology 46, no. 5 (October 2002): 564–82. http://dx.doi.org/10.1006/jmps.2002.1414.

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Kim, Bong Je. "Analysis of Characteristics of Unification Education Research Using Multidimensional Scaling." Journal of Moral & Ethics Education 60 (August 31, 2018): 291–320. http://dx.doi.org/10.18338/kojmee.2018..60.291.

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46

KURLYANDSKII, Viktor V., and Aleksandr N. BILANENKO. "Using the multidimensional scaling method when assessing the financial feasibility of including foreign exchange market assets in securities portfolios." Finance and Credit 29, no. 7 (July 31, 2023): 1595–614. http://dx.doi.org/10.24891/fc.29.7.1595.

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Subject. This article discusses the application of the multidimensional scaling method to improve the methods of formation and effective management of a portfolio of securities. Objectives. The article aims to prove the rationality of using the multidimensional scaling method to assess the financial feasibility of including foreign exchange market assets in securities portfolios. Methods. For the study, we used the methods of correlation analysis and multidimensional scaling. Results. The article finds that the use of the multidimensional scaling method helps identify similar features of the assets of the securities market and the foreign exchange market, and confirms the rationality of using this method when assessing the financial feasibility of including foreign exchange market assets in securities portfolios. Conclusions. The article concludes that it is necessary to make changes to the traditional decision-making model in portfolio investment, recognizing the use of multidimensional scaling to assess the economic feasibility of including foreign exchange market assets denominated in the same currency as portfolio assets in securities portfolios as rational.

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Kobayashi, Yuh, Hideki Takayasu, Shlomo Havlin, and Misako Takayasu. "Robust Characterization of Multidimensional Scaling Relations between Size Measures for Business Firms." Entropy 23, no. 2 (January 29, 2021): 168. http://dx.doi.org/10.3390/e23020168.

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Although the sizes of business firms have been a subject of intensive research, the definition of a “size” of a firm remains unclear. In this study, we empirically characterize in detail the scaling relations between size measures of business firms, analyzing them based on allometric scaling. Using a large dataset of Japanese firms that tracked approximately one million firms annually for two decades (1994–2015), we examined up to the trivariate relations between corporate size measures: annual sales, capital stock, total assets, and numbers of employees and trading partners. The data were examined using a multivariate generalization of a previously proposed method for analyzing bivariate scalings. We found that relations between measures other than the capital stock are marked by allometric scaling relations. Power–law exponents for scalings and distributions of multiple firm size measures were mostly robust throughout the years but had fluctuations that appeared to correlate with national economic conditions. We established theoretical relations between the exponents. We expect these results to allow direct estimation of the effects of using alternative size measures of business firms in regression analyses, to facilitate the modeling of firms, and to enhance the current theoretical understanding of complex systems.

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Kėdaitienė, Angelė, and Vytautas Kėdaitis. "Multidimensional Scaling in Market Research: Advantages and Disadvantages." Lietuvos statistikos darbai 49, no. 1 (December 20, 2010): 52–61. http://dx.doi.org/10.15388/ljs.2010.13948.

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 Multidimensional scaling was developed by psychometricians, namely R. N. Shepard (1962) and J. B. Kruskal (1964). Its purpose is to deduce indirectly the dimensions a respondent uses to evaluate alternatives. The reason for using the indirect approach is that, in many cases, the attributes may be unknown and respondents may be unable or unwilling to represent their reasons accurately. As already mentioned, multidimensional scaling requires an object-by-object similarity matrix as an input. Initially popularized, however, multidimensional scaling relies on judged similarity. That is, respondents indicate how similar pairs of objects are directly rated (e.g. on a 1–10 scale). This can be a burdensome task since for p objects p(p-1)/2judgments are needed. Still, the use of similarity judgments is relatively easy for respondents, especially when they cannot or do not want to reveal the basis for their opinion. The results of multidimensional scaling depend on (a) the sample chosen to judge similarity and (b) the objects whose similarity is judged and the quality of input data. Multidimensional scaling derives dimensions that appear to be used by those rating a particular set of objects. The basic type of multidimensional scaling involves deducing graphical models of alternatives (e.g. brands) alone (simple space) from similarity data. Some early applications of multidimensional scaling accepted apparent dimensions as “truth” without question or validation, which often proved to be disastrous. It is advisable to use multidimensional scaling as a generator of hypotheses rather than as a final model of the market. Any important result should be confirmed on a separate sample with a separate method, such as direct questioning, before the results are given too much credence. Multidimensional scaling generates a configuration in which the relative positions of the brands are unique. The picture can be changed by several operations without changing the relationship among the interpoint distance in some of the algorithms (assuming the Euclidean distance is used, which it almost always is). A major problem in data collection is the burden on respondents as the number of alternatives increases (e.g. 20 alternatives require 190 pairs). However, if respondents are “homogeneous”, it is possible to have different subjects rate a different pair.

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Huang, Jih-Jeng, Gwo-Hshiung Tzeng, and Chorng-Shyong Ong. "Multidimensional data in multidimensional scaling using the analytic network process." Pattern Recognition Letters 26, no. 6 (May 2005): 755–67. http://dx.doi.org/10.1016/j.patrec.2004.09.027.

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Huh, Myung-Hoe, and Yong-Goo Lee. "Multidimensional Scaling of Asymmetric Distance Matrices." Korean Journal of Applied Statistics 25, no. 4 (August 31, 2012): 613–20. http://dx.doi.org/10.5351/kjas.2012.25.4.613.

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