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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 30. Juli 2024

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1

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

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2

Jeffers, J. N. R., und Mark L. Davison. „Multidimensional Scaling.“ Statistician 34, Nr. 2 (1985): 257. http://dx.doi.org/10.2307/2988171.

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3

Jolliffe, Ian. „Multidimensional Scaling“. Technometrics 38, Nr. 4 (November 1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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4

Mugavin, Marie E. „Multidimensional Scaling“. Nursing Research 57, Nr. 1 (Januar 2008): 64–68. http://dx.doi.org/10.1097/01.nnr.0000280659.88760.7c.

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5

Hout, Michael C., Megan H. Papesh und Stephen D. Goldinger. „Multidimensional scaling“. Wiley Interdisciplinary Reviews: Cognitive Science 4, Nr. 1 (08.10.2012): 93–103. http://dx.doi.org/10.1002/wcs.1203.

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6

Lee, In-Soon. „Multidimensional Scaling“. Journal of Korean Medical Library Association 19, Nr. 1 (Juni 1992): 1–6. http://dx.doi.org/10.69528/jkmla.1992.19.1.1.

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7

Aflalo, Y., und R. Kimmel. „Spectral multidimensional scaling“. Proceedings of the National Academy of Sciences 110, Nr. 45 (09.10.2013): 18052–57. http://dx.doi.org/10.1073/pnas.1308708110.

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8

Venna, Jarkko, und Samuel Kaski. „Local multidimensional scaling“. Neural Networks 19, Nr. 6-7 (Juli 2006): 889–99. http://dx.doi.org/10.1016/j.neunet.2006.05.014.

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9

Spence, Ian, und Stephan Lewandowsky. „Robust multidimensional scaling“. Psychometrika 54, Nr. 3 (September 1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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10

de Leeuw, Jan, und Patrick J. F. Groenen. „Inverse Multidimensional Scaling“. Journal of Classification 14, Nr. 1 (01.01.1997): 3–21. http://dx.doi.org/10.1007/s003579900001.

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11

Rajawat, Ketan, und Sandeep Kumar. „Stochastic Multidimensional Scaling“. IEEE Transactions on Signal and Information Processing over Networks 3, Nr. 2 (Juni 2017): 360–75. http://dx.doi.org/10.1109/tsipn.2017.2668145.

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12

Hébert, Pierre-Alexandre, Marie-Hélène Masson und Thierry Denœux. „Fuzzy multidimensional scaling“. Computational Statistics & Data Analysis 51, Nr. 1 (November 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 und I. Yavneh. „Multigrid multidimensional scaling“. Numerical Linear Algebra with Applications 13, Nr. 2-3 (2006): 149–71. http://dx.doi.org/10.1002/nla.475.

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14

Walundungo, Gloria, Marline Paendong und Tohap Manurung. „Penggunaan Analisis Multidimensional Scaling Untuk Mengetahui Kemiripan Rumah Makan Di Manado Town Square Berdasarkan Kerakteristik Pelanggan“. d'CARTESIAN 3, Nr. 1 (30.03.2014): 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, Nr. 23-24 (Mai 2009): 2001–6. http://dx.doi.org/10.1016/j.physleta.2009.04.007.

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16

Bakker, Ryan, und Keith T. Poole. „Bayesian Metric Multidimensional Scaling“. Political Analysis 21, Nr. 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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17

Dzhafarov, Ehtibar N., und Hans Colonius. „Multidimensional Fechnerian Scaling: Basics“. Journal of Mathematical Psychology 45, Nr. 5 (Oktober 2001): 670–719. http://dx.doi.org/10.1006/jmps.2000.1341.

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18

Zhang, Zhidong, und Luis Garcia. „Examining Dimensionality and Validity of the Academic Integrity Survey Instrument“. Journal of Education and Development 7, Nr. 1 (08.02.2023): 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 und Ping Fan. „Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI“. Modern Physics Letters B 31, Nr. 17 (14.06.2017): 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

Kim, Bong Je. „Analysis of Characteristics of Unification Education Research Using Multidimensional Scaling“. Journal of Moral & Ethics Education 60 (31.08.2018): 291–320. http://dx.doi.org/10.18338/kojmee.2018..60.291.

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21

Dourado, António, Paulo Barbeiro, Edgar Ferreira, Luís Amaral, António Coelho und Dora Nogueira. „VISBREAKER ANALYSIS BY MULTIDIMENSIONAL SCALING“. IFAC Proceedings Volumes 40, Nr. 9 (2007): 356–61. http://dx.doi.org/10.3182/20070723-3-pl-2917.00058.

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22

Lee, Su-Gi, Yong-Seok Choi und Bo-Hui Lee. „Visualizations of Asymmetric Multidimensional Scaling“. Korean Journal of Applied Statistics 27, Nr. 4 (31.08.2014): 619–27. http://dx.doi.org/10.5351/kjas.2014.27.4.619.

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23

Ueda, Tohru. „Sensitivity Analysis in Multidimensional Scaling“. Behaviormetrika 16, Nr. 25 (Januar 1989): 35–47. http://dx.doi.org/10.2333/bhmk.16.25_35.

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24

Moinpour, Reza, Paul E. Green, Frank J. Carmone und Scott M. Smith. „Multidimensional Scaling: Concepts and Applications“. Journal of Marketing Research 28, Nr. 4 (November 1991): 504. http://dx.doi.org/10.2307/3172796.

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25

Cox, Trevor F., und Michael A. A. Cox. „Multidimensional scaling on a sphere“. Communications in Statistics - Theory and Methods 20, Nr. 9 (Januar 1991): 2943–53. http://dx.doi.org/10.1080/03610929108830679.

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26

Huang, Yameng, und Zhouchen Lin. „Binary Multidimensional Scaling for Hashing“. IEEE Transactions on Image Processing 27, Nr. 1 (Januar 2018): 406–18. http://dx.doi.org/10.1109/tip.2017.2759250.

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27

Buja, Andreas, Deborah F. Swayne, Michael L. Littman, Nathaniel Dean, Heike Hofmann und Lisha Chen. „Data Visualization With Multidimensional Scaling“. Journal of Computational and Graphical Statistics 17, Nr. 2 (Juni 2008): 444–72. http://dx.doi.org/10.1198/106186008x318440.

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28

Perales, E., F. J. Burgos, M. Vilaseca, V. Viqueira und F. M. Martínez-Verdú. „Graininess characterization by multidimensional scaling“. Journal of Modern Optics 66, Nr. 9 (19.03.2019): 929–38. http://dx.doi.org/10.1080/09500340.2019.1589006.

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29

Saeed, Nasir, Haewoon Nam, Mian Imtiaz Ul Haq und Dost Bhatti Muhammad Saqib. „A Survey on Multidimensional Scaling“. ACM Computing Surveys 51, Nr. 3 (16.07.2018): 1–25. http://dx.doi.org/10.1145/3178155.

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30

Cox, Trevor F., Michael A. A. Cox und Joao A. Branco. „Multidimensional scaling for n-tuples“. British Journal of Mathematical and Statistical Psychology 44, Nr. 1 (Mai 1991): 195–206. http://dx.doi.org/10.1111/j.2044-8317.1991.tb00955.x.

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31

Rohde, Douglas L. T. „Methods for Binary Multidimensional Scaling“. Neural Computation 14, Nr. 5 (01.05.2002): 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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32

Forero, Pedro A., und Georgios B. Giannakis. „Sparsity-Exploiting Robust Multidimensional Scaling“. IEEE Transactions on Signal Processing 60, Nr. 8 (August 2012): 4118–34. http://dx.doi.org/10.1109/tsp.2012.2197617.

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33

Sato-Ilic, Mika, und 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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34

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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35

Le, Huiling, und Christopher G. Small. „Multidimensional scaling of simplex shapes“. Pattern Recognition 32, Nr. 9 (September 1999): 1601–13. http://dx.doi.org/10.1016/s0031-3203(99)00023-0.

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36

Jackson, J. Edward. „Key Texts in Multidimensional Scaling“. Technometrics 27, Nr. 1 (Februar 1985): 88. http://dx.doi.org/10.1080/00401706.1985.10488020.

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37

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

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38

Gower, John C., und Roger F. Ngouenet. „Nonlinearity effects in multidimensional scaling“. Journal of Multivariate Analysis 94, Nr. 2 (Juni 2005): 344–65. http://dx.doi.org/10.1016/j.jmva.2004.05.008.

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39

Bae, Seung-Hee, Judy Qiu und 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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40

Cozzens, Margaret B., und Rochelle Leibowitz. „Multidimensional scaling and threshold graphs“. Journal of Mathematical Psychology 31, Nr. 2 (Juni 1987): 179–91. http://dx.doi.org/10.1016/0022-2496(87)90014-9.

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41

Buja, Andreas, und Deborah F. Swayne. „Visualization Methodology for Multidimensional Scaling“. Journal of Classification 19, Nr. 1 (01.01.2002): 7–43. http://dx.doi.org/10.1007/s00357-001-0031-0.

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42

Cox, Mitchell A. A., und Trevor F. Cox. „Interpreting stress in multidimensional scaling“. Journal of Statistical Computation and Simulation 37, Nr. 3-4 (Dezember 1990): 211–23. http://dx.doi.org/10.1080/00949659008811305.

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43

Goodrum, Abby A. „Multidimensional scaling of video surrogates“. Journal of the American Society for Information Science and Technology 52, Nr. 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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44

Fenton, Mark, und Philip Pearce. „Multidimensional scaling and tourism research“. Annals of Tourism Research 15, Nr. 2 (Januar 1988): 236–54. http://dx.doi.org/10.1016/0160-7383(88)90085-0.

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45

Dzhafarov, Ehtibar N. „Multidimensional Fechnerian Scaling: Perceptual Separability“. Journal of Mathematical Psychology 46, Nr. 5 (Oktober 2002): 564–82. http://dx.doi.org/10.1006/jmps.2002.1414.

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46

KURLYANDSKII, Viktor V., und 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, Nr. 7 (31.07.2023): 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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47

Kobayashi, Yuh, Hideki Takayasu, Shlomo Havlin und Misako Takayasu. „Robust Characterization of Multidimensional Scaling Relations between Size Measures for Business Firms“. Entropy 23, Nr. 2 (29.01.2021): 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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48

Huang, Jih-Jeng, Gwo-Hshiung Tzeng und Chorng-Shyong Ong. „Multidimensional data in multidimensional scaling using the analytic network process“. Pattern Recognition Letters 26, Nr. 6 (Mai 2005): 755–67. http://dx.doi.org/10.1016/j.patrec.2004.09.027.

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49

Huh, Myung-Hoe, und Yong-Goo Lee. „Multidimensional Scaling of Asymmetric Distance Matrices“. Korean Journal of Applied Statistics 25, Nr. 4 (31.08.2012): 613–20. http://dx.doi.org/10.5351/kjas.2012.25.4.613.

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50

McFarlane, Mary, und Forrest W. Young. „Graphical Sensitivity Analysis for Multidimensional Scaling“. Journal of Computational and Graphical Statistics 3, Nr. 1 (März 1994): 23. http://dx.doi.org/10.2307/1390793.

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