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Thematische Bibliographien / Multidimensional scaling / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Multidimensional scaling“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 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 (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 (1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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5

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

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6

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

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7

Aflalo, Y., and R. Kimmel. "Spectral multidimensional scaling." Proceedings of the National Academy of Sciences 110, no. 45 (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 (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 (1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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10

de Leeuw, Jan, and Patrick J. F. Groenen. "Inverse Multidimensional Scaling." Journal of Classification 14, no. 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 (2017): 360–75. http://dx.doi.org/10.1109/tsipn.2017.2668145.

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12

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

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13

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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14

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 (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 othe

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15

Andrecut, M. "Molecular dynamics multidimensional scaling." Physics Letters A 373, no. 23-24 (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 rota

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17

Dzhafarov, Ehtibar N., and Hans Colonius. "Multidimensional Fechnerian Scaling: Basics." Journal of Mathematical Psychology 45, no. 5 (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 (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 loo

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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 (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 t

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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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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

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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 (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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33

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 (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 (1985): 88. http://dx.doi.org/10.1080/00401706.1985.10488020.

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36

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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37

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

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38

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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39

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

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40

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

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41

Cox, Mitchell A. A., and Trevor F. Cox. "Interpreting stress in multidimensional scaling." Journal of Statistical Computation and Simulation 37, no. 3-4 (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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43

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

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44

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

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45

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 (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 a

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47

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 (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 exa

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48

Kėdaitienė, Angelė, and Vytautas Kėdaitis. "Multidimensional Scaling in Market Research: Advantages and Disadvantages." Lietuvos statistikos darbai 49, no. 1 (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. Tha

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49

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 (2005): 755–67. http://dx.doi.org/10.1016/j.patrec.2004.09.027.

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50

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

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