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Journal articles on the topic 'The method of multidimensional scaling'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

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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MIYANO, Hisao. "Multidimensional Scaling Method for Asymmetric Similarity Data." Transactions of the Society of Instrument and Control Engineers 26, no. 1 (1990): 87–92. http://dx.doi.org/10.9746/sicetr1965.26.87.

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Manjunatha, B., Appaji Pundalik Naik, K. R. Mahendra, et al. "Multidimensional Scaling Method and Some Practical Applications." Archives of Current Research International 24, no. 6 (2024): 586–99. http://dx.doi.org/10.9734/acri/2024/v24i6814.

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Multi-Dimensional Scaling (MDS) is a data visualization method that identifies clusters of points by representing the distances or dissimilarities between sets of objects in a lower-dimensional space. This paper explores the theoretical concepts of MDS, various methods of implementation, and the analytical processes involved. Emphasis is placed on the "Stress" function, a goodness-of-fit metric that quantifies the discrepancy between distances in high-dimensional and lower-dimensional spaces. Practical examples and detailed procedures for implementing MDS using MS-Excel and R are provided to e

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Walesiak, Marek. "The Choice of Groups of Variable Normalization Methods in Multidimensional Scaling." Przegląd Statystyczny 63, no. 1 (2016): 7–18. http://dx.doi.org/10.5604/01.3001.0014.1145.

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In multidimensional scaling carried out on the basis of metric data matrix (interval, ratio) one of the stages is the choice of the variable normalization method. The R package clusterSim with data. Normalization function has been developed for that purpose. It provides 18 data normalization methods. In this paper the proposal of procedure which allows to isolate groups of normalization methods that lead to similar multidimensional scaling results were presented. The proposal can reduce the problem of choosing the normalization method in multidimensional scaling. The results are illustrated vi

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Xuan, Qi, Xiaodi Ma, Chenbo Fu, Hui Dong, Guijun Zhang, and Li Yu. "Heterogeneous multidimensional scaling for complex networks." International Journal of Modern Physics C 26, no. 02 (2015): 1550023. http://dx.doi.org/10.1142/s0129183115500230.

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Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better

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Wawrosz, Petr, and David Křížek. "Visualisation of linear ordering results using multidimensional scaling – problems and an overview of studies." Argumenta Oeconomica 54, no. 1 (2025): 204–24. https://doi.org/10.15611/aoe.2025.1.13.

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Aim: The aim of the article was to review the methodological solutions proposed as part of the hybrid method, which combines linear ordering with multidimensional scaling for various types of data. In the first step, after applying multidimensional scaling, it was possible to visualise objects of interest in a two-dimensional space. In the second step, the objects were linearly ordered according to an aggregate measure based on the Euclidean distance. Methodology: The general procedure of the hybrid method, which can be used to visualise results of linear ordering for metric, ordinal and inter

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Walesiak, Marek, Grażyna Dehnel, and Andrzej Dudek. "Visualisation of linear ordering results using multidimensional scaling – problems and an overview of studies." Argumenta Oeconomica 54, no. 1 (2025): 187–203. https://doi.org/10.15611/aoe.2025.1.12.

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Aim: The aim of the article was to review the methodological solutions proposed as part of the hybrid method, which combines linear ordering with multidimensional scaling for various types of data. In the first step, after applying multidimensional scaling, it was possible to visualise objects of interest in a two-dimensional space. In the second step, the objects were linearly ordered according to an aggregate measure based on the Euclidean distance. Methodology: The general procedure of the hybrid method, which can be used to visualise results of linear ordering for metric, ordinal and inter

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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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Saito, Takayuki, and Shin-ichi Takeda. "Multidimensional Scaling of Asymmetric Proximity: Model and Method." Behaviormetrika 17, no. 28 (1990): 49–80. http://dx.doi.org/10.2333/bhmk.17.28_49.

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10

de Leeuw, Jan. "Convergence of the majorization method for multidimensional scaling." Journal of Classification 5, no. 2 (1988): 163–80. http://dx.doi.org/10.1007/bf01897162.

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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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Moroz, Kaleriya, Elena Kizhevatova, Vitaly Omelchenko, Karina Karakhanyan, and Natalia Korotkieva. "Application of multidimensional scaling method for analysis of EEG features in patients with dyscirculatory encephalopathy." E3S Web of Conferences 210 (2020): 17005. http://dx.doi.org/10.1051/e3sconf/202021017005.

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The object of our work is to test the possibility of using the method of multidimensional scaling to identify the features of EEG in order to classify subjects in patients with dyscirculatory encephalopathy and healthy people by establishing differences in the graphs of the dynamics of the location of vectors in the multidimensional coordinate space. For healthy subjects, in almost all tests, there is a coincidence or close location of the spatial coordinates of the multidimensional scaling. For patients with dyscirculatory encephalopathy, there is no marked coincidence of coordinates; however

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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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Preoteasa, Cristina Teodora, Ecaterina Ionescu, Elena Preoteasa, J. A. Tenreiro Machado, Mihaela Cristina Baleanu, and Dumitru Baleanu. "Multidimensional Scaling for Orthodontic Root Resorption." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/383698.

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The paper investigates the risk factors for the severity of orthodontic root resorption. The multidimensional scaling (MDS) visualization method is used to investigate the experimental data from patients who received orthodontic treatment at the Department of Orthodontics and Dentofacial Orthopedics, Faculty of Dentistry, “Carol Davila” University of Medicine and Pharmacy, during a period of 4 years. The clusters emerging in the MDS plots reveal features and properties not easily captured by classical statistical tools. The results support the adoption of MDS for tackling the dentistry informa

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Prihatin, Edwin, and Andi Nur Rahman. "PEMILIHAN GENTENG RUMAH MENGGUNAKAN METODE MULTIDIMENSIONAL SCALING." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 3, no. 3 (2022): 478–92. http://dx.doi.org/10.46306/lb.v3i3.156.

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Tile is one of the most important building materials. Tile has the main function as a roof covering. Tile is more often used in the construction of houses. The tile protects the occupants of the house from the hot sun and rain. Selection of the right tile needs to be considered for the comfort of the occupants of the house. In this study, the determining factors in the selection of roof tiles are price, product quality, and product availability. The types of tiles used in this research are ceramic tiles, clay tiles, Alderon tiles, asbestos tiles, zinc tiles, and metal roof tiles. The method us

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Zaborski, Artur. "The Use of the Incomplete Tetrad Method for Measuring the Similarities in Nonmetric Multidimensional Scaling." Folia Oeconomica Stetinensia 20, no. 1 (2020): 519–30. http://dx.doi.org/10.2478/foli-2020-0030.

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AbstractResearch background: So far, many methods of direct measurement of similarity in multidimensional scaling have been developed (e.g. ranking, sorting, pairwise comparison and others). The method selection affects the subjective feelings of the respondents, i.e. fatigue, weariness resulting from making numerous assessments, or difficulties in expressing similarity assessments.Purpose: In the proposed method, for all four-element sets (tetrads) of objects a respondent is asked to pick out the most similar and the least similar pair. Because the number of tetrads increases very rapidly wit

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Lopes, António M., J. A. Tenreiro Machado, and Alexandra M. Galhano. "Multidimensional Scaling Visualization Using Parametric Entropy." International Journal of Bifurcation and Chaos 25, no. 14 (2015): 1540017. http://dx.doi.org/10.1142/s0218127415400179.

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This paper studies complex systems using a generalized multidimensional scaling (MDS) technique. Complex systems are characterized by time-series responses, interpreted as a manifestation of their dynamics. Two types of time-series are analyzed, namely 18 stock markets and the gross domestic product per capita of 18 countries. For constructing the MDS charts, indices based on parametric entropies are adopted. Multiparameter entropies allow the variation of the parameters leading to alternative sets of charts. The final MDS maps are then assembled by means of Procrustes’ method that maximizes t

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Moon, Junghoon, Cheul Rhee, Hyunjeong Kang, and G. Lawrence Sanders. "Gaining Insight into Cognitive Structure Using GALILEO Method." Journal of Electronic Commerce in Organizations 8, no. 1 (2010): 26–40. http://dx.doi.org/10.4018/jeco.2010103002.

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In this article we introduce a multidimensional systems evaluation technique for tapping into the group cognitive structure. The objective is to illustrate how GALILEO assists in mapping the multidimensional cognitive domain of user evaluations in order to subsequently identify strategies to build customer loyalty and lock-in with e-commerce websites. A popular approach for understanding the structure of relationships between constructs is Multi-Dimensional Scaling (MDS). GALILEO is a very powerful multidimensional scaling technique developed by researchers in the area of communications and co

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Walesiak, Marek, and Andrzej Dudek. "Selecting the optimal multidimensional scaling procedure for metric data with R environment." Statistics in Transition new series 18, no. 3 (2017): 521–40. http://dx.doi.org/10.59170/stattrans-2017-027.

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In multidimensional scaling (MDS) carried out on the basis of a metric data matrix (interval, ratio), the main decision problems relate to the selection of the method of normalization of the values of the variables, the selection of distance measure and the selection of MDS model. The article proposes a solution that allows choosing the optimal multidimensional scaling procedure according to the normalization methods, distance measures and MDS model applied. The study includes 18 normalization methods, 5 distance measures and 3 types of MDS models (ratio, interval and spline). It uses two crit

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Groenen, Patrick J. F., and Willem J. Heiser. "The tunneling method for global optimization in multidimensional scaling." Psychometrika 61, no. 3 (1996): 529–50. http://dx.doi.org/10.1007/bf02294553.

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21

Hoffman, Donna L., and Jan De Leeuw. "Interpreting multiple correspondence analysis as a multidimensional scaling method." Marketing Letters 3, no. 3 (1992): 259–72. http://dx.doi.org/10.1007/bf00994134.

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HOJO, HIROSHI. "A new nonmetric multidimensional scaling method for sorting data." Japanese Psychological Research 35, no. 3 (1993): 129–39. http://dx.doi.org/10.4992/psycholres1954.35.129.

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23

Marvi, Mohammad Hossein, Morteza Maleki Minbashrazgah, Azim Zarei, and Golnar Shojaei Baghini. "Knowledge foundation in green purchase behaviour: Multidimensional scaling method." Cogent Business & Management 7, no. 1 (2020): 1773676. http://dx.doi.org/10.1080/23311975.2020.1773676.

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Kang, In-Ho, and Sang-Yun Seo. "Masan Chrysanthemum Festival Image Analysis Using Multidimensional Scaling Method." Regional Industry Review 45, no. 4 (2022): 141–62. http://dx.doi.org/10.33932/rir.45.4.7.

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Li, Taoying, Qian Yin, Runyu Song, Mingyue Gao, and Yan Chen. "Multidimensional scaling method for prediction of lysine glycation sites." Computing 101, no. 6 (2019): 705–24. http://dx.doi.org/10.1007/s00607-019-00710-x.

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Papazoglou, Sofia, and Kostas Mylonas. "An examination of Alternative Multidimensional Scaling Techniques." Educational and Psychological Measurement 77, no. 3 (2016): 429–48. http://dx.doi.org/10.1177/0013164416661823.

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The purpose of this study is to compare alternative multidimensional scaling (MDS) methods for constraining the stimuli on the circumference of a circle and on the surface of a sphere. Specifically, the existing MDS-T method for plotting the stimuli on the circumference of a circle is applied, and its extension is proposed for constraining the stimuli on the surface of a sphere. The data analyzed come from previous research and concerns Maslach and Jackson’s burnout syndrome and Holland’s vocational personality types. The configurations for the same data on the circle and the sphere shared sim

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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 (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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Aileni, Raluca Maria, and Cristina Stroe. "Multidimensional analysis of textiles coated with electroactive polymers for actuators." Industria Textila 75, no. 05 (2024): 550–55. http://dx.doi.org/10.35530/it.075.05.202332.

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This paper presents a multidimensional analysis of textiles coated with conductive polymers for actuators. The purpose of using multidimensional scaling analysis is to compare similarities and dissimilarities between conductive textiles obtained through conductive polymeric film deposition to observe the optimal value for electrical resistance and to select an adequate method to achieve conductive fabric using different polymers (polyethylene glycol, polyvinyl alcohol or polyvinylidene fluoride) and metal microparticles (copper or nickel). The multidimensional analysis is based on mapping a se

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Jamróz, Dariusz. "Application of Multidimensional Scaling to Classification of Various Types of Coal/Zastosowanie Skalowania Wielowymiarowego Do Klasyfikacji Różnych Typów Węgli." Archives of Mining Sciences 59, no. 2 (2014): 413–25. http://dx.doi.org/10.2478/amsc-2014-0029.

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Abstract Visualization of multidimensional data is a new way of statistical analysis of so-called statistical graphical methods. These methods allow to classify some analyzed objects, including their various features. Facing grained materials problems, like coal or ores many characteristics have an influence on the quality of product. In case of coal, many features must be taken into consideration to determine quality of the material. Apart from most obvious characteristics like particle size, particle density or ash contents there are many others which cause significant differences between co

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McCloughlin, Thomas. "CLOSENESS OF FIT TO THE IDEAL: CLASSIFICATION AND SIMILARITY OF TEACHERS’ REFLECTIONS USING MULTI-DIMENSIONAL SCALING." Problems of Education in the 21st Century 67, no. 1 (2015): 72–80. http://dx.doi.org/10.33225/pec/15.67.72.

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The objectives of this research are to provide the theoretical basis for the analysis of qualitative data, such as teachers' responses to reflective questions using quantitative methods such as multidimensional scaling. The starting point is Estes' assumption that classification is basic to all intellectual activities, and since reflections are artefacts of intellectual activities, they should be amenable to classificatory methods such as multidimensional scaling. Similarity relates to distance between perceptions and MDS can utilise this feature. By way of example, N=29 teachers - graduates,

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Li, James Xinzhi. "Visualization of High-Dimensional Data with Relational Perspective Map." Information Visualization 3, no. 1 (2004): 49–59. http://dx.doi.org/10.1057/palgrave.ivs.9500051.

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This paper introduces a method called relational perspective map (RPM) to visualize distance information in high-dimensional spaces. Like conventional multidimensional scaling, the RPM algorithm aims to produce proximity preserving 2-dimensional (2-D) maps. The main idea of the RPM algorithm is to simulate a multiparticle system on a closed surface: whereas the repulsive forces between the particles reflect the distance information, the closed surface holds the whole system in balance and prevents the resulting map from degeneracy. A special feature of RPM algorithm is its ability to partition

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YAMAMOTO, Kouji, Tsuyoshi HATORI, Koichi OKADA, Kazuya AOKI, and Kiyoshi KOBAYASHI. "Evaluation Method for Infrastructural Asset Management Based on Multidimensional Scaling." Journal of Construction Management, JSCE 15 (2008): 115–30. http://dx.doi.org/10.2208/procm.15.115.

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Nurhayati, Ai, and Frencius . "Mapping perception of consumer antivirus software with multidimensional scaling method." APTIKOM Journal on Computer Science and Information Technologies 4, no. 3 (2020): 91–95. http://dx.doi.org/10.34306/csit.v4i3.96.

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Antivirus software industry is growing rapidly in the world in 2018. The domestic antivirus software industrymust be able to compete on a global scale. To face free trade, Indonesia's antivirus software industry must be able toknow its position in the minds of consumers, especially domestic consumers. In this research, Smadav will representthe antivirus software industry from Indonesia. In this research want to know how the position of smadav comparedwith its current competitors, namely Avast, Avira, AVG, Kaspersky, McAfee and Norton. This research is only done tomap antivirus software based o

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정병국. "A Study on Hybrid Camera Positioning by Multidimensional Scaling Method." A Journal of Brand Design Association of Korea 10, no. 2 (2012): 83–94. http://dx.doi.org/10.18852/bdak.2012.10.2.83.

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Shin, Sang Min, Eun-Seong Kim, and Yong-Seok Choi. "Multidimensional Scaling Using the Pseudo-Points Based on Partition Method." Korean Journal of Applied Statistics 28, no. 6 (2015): 1171–80. http://dx.doi.org/10.5351/kjas.2015.28.6.1171.

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Žilinskas, Antanas, and Julius Žilinskas. "A hybrid method for multidimensional scaling using city-block distances." Mathematical Methods of Operations Research 68, no. 3 (2008): 429–43. http://dx.doi.org/10.1007/s00186-008-0238-5.

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Nurhayati, Ai, and Frencius Frencius. "Mapping Perception of Consumer Antivirus Software with Multidimensional Scaling Method." APTIKOM Journal on Computer Science and Information Technologies 4, no. 3 (2019): 91–95. http://dx.doi.org/10.11591/aptikom.j.csit.13.

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Antivirus software industry is growing rapidly in the world in 2018. The domestic antivirus software industry must be able to compete on a global scale. To face free trade, Indonesia's antivirus software industry must be able to know its position in the minds of consumers, especially domestic consumers. In this research, Smadav will represent the antivirus software industry from Indonesia.In this research want to know how the position of smadav compared with its current competitors, namely Avast, Avira, AVG, Kaspersky, McAfee and Norton. This research is only done to map antivirus software bas

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Vera, José Fernando. "Distance stability analysis in multidimensional scaling using the jackknife method." British Journal of Mathematical and Statistical Psychology 70, no. 1 (2016): 25–41. http://dx.doi.org/10.1111/bmsp.12079.

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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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Mohammed Ramadan, Ayad. "Ranking of Fuzzy Numbers by using Scaling Method." passer 3, no. 2 (2019): 137–43. http://dx.doi.org/10.24271/psr.24.

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In this paper, we presented for the first time a multidimensional scaling approach to find the scaling as well as the ranking of triangular fuzzy numbers. Each fuzzy number was represented by a row in a matrix, and then found the configuration points (scale points) which represent the fuzzy numbers in . Since these points are not uniquely determined, then we presented different techniques to reconfigure the points to compare them with other methods. The results showed the ability of ranking fuzzy numbers

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Costa, António C., J. A. Tenreiro Machado, and Maria Dulce Quelhas. "Multidimensional Scaling Applied to Histogram-Based DNA Analysis." Comparative and Functional Genomics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/289694.

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This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendallτrank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of st

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KURLYANDSKII, Viktor V., and Anton A. ZAITSEV. "Multidimensional scaling in the analysis of the time structure of zero-coupon yield of government securities." Financial Analytics: Science and Experience 18, no. 1 (2025): 21–29. https://doi.org/10.24891/fa.18.1.21.

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Subject. This article deals with the multidimensional scaling as a methodological basis for improving the generally accepted practice of describing the time structure of interest rates for homogeneous financial instruments with the same qualitative characteristics, and for assessing the financial feasibility of revising the time structure of government bond issues. Objectives. The article aims to prove the financial feasibility of revising the time structure of government bond issues. Methods. For the study, we used statistical methods of data analysis, multidimensional scaling, and regression

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Qin, Shuang, Qun Wan, and Lin Fu Duan. "Minimum Norm Multidimensional Scaling Solution for Mobile Station Localization in Wireless Sensors Network." Advanced Materials Research 472-475 (February 2012): 106–9. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.106.

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Mobile station (MS) localization which plays an important role in the process of target continuous localization has received considerable attention. In this paper, a novel multidimensional scaling (MDS) method based on Minimum norm solution is proposed for passive emitter location in wireless networks. It is shown that the proposed approach is a generalization of the mobile localization method based on multidimensional scaling (MDS) analysis. Through computer simulations, we can see that the proposed algorithm has a better performance compared with classic MDS and subspace algorithms in time o

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Molytė, Alma, Alina Urnikytė, and Vaidutis Kučinskas. "A comparative analysis of mathematical methods for homogeneity estimation of the Lithuanian population." Acta medica Lituanica 26, no. 4 (2020): 211–16. http://dx.doi.org/10.6001/actamedica.v26i4.4206.

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Background. Population genetic structure is one of the most important population genetic parameters revealing its demographic features. The aim of this study was to evaluate the homogeneity of the Lithuanian population on the basis of the genome-wide genotyping data. The comparative analysis of three methods – multidimensional scaling, principal components, and principal coordinates analysis – to visualize multidimensional genetics data was performed. The results of visualization (mapping images) are also presented. Materials and methods. The data set consisted of 425 samples from six ethnolin

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HE, GUANGHUI, LINGFENG ZHANG, and ZHAOWEI SHANG. "CORRELATION-BASED MULTIDIMENSIONAL SCALING FOR UNSUPERVISED SUBSPACE LEARNING." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 03 (2012): 1250030. http://dx.doi.org/10.1142/s0219691312500300.

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Multidimensional scaling (MDS) has been applied in many applications such as dimensionality reduction and data mining. However, one of the drawbacks of MDS is that it is only defined on "training" data without clear extension to out-of-sample points. Furthermore, since that MDS is based on Euclidean distance (which is a dissimilarity measure), it is not suitable for detecting the nonlinear manifold structure embedded in the similarities between data points. In this paper, we extend MDS to the correlation measure space, named correlation MDS (CMDS). CMDS employs an explicit nonlinear mapping be

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Li, Xueming, and Stephen G. Sireci. "A New Method for Analyzing Content Validity Data Using Multidimensional Scaling." Educational and Psychological Measurement 73, no. 3 (2013): 365–85. http://dx.doi.org/10.1177/0013164412473825.

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Howson, Phil J., and Philip J. Monahan. "A method for comparing perceptual distances and areas with multidimensional scaling." MethodsX 7 (2020): 100790. http://dx.doi.org/10.1016/j.mex.2020.100790.

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杜, 梦. "Visualization Method of Learners’ Cognitive Structure Based on Multidimensional Scaling Analysis." Creative Education Studies 08, no. 06 (2020): 867–72. http://dx.doi.org/10.12677/ces.2020.86143.

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NAGINO, G., M. SHOZAKAI, T. TODA, H. SARUWATARI, and K. SHIKANO. "Building an Effective Speech Corpus by Utilizing Statistical Multidimensional Scaling Method." IEICE Transactions on Information and Systems E91-D, no. 3 (2008): 607–14. http://dx.doi.org/10.1093/ietisy/e91-d.3.607.

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Jamroz, Dariusz. "The perspective-based observational tunnels method: A new method of multidimensional data visualization." Information Visualization 16, no. 4 (2017): 346–60. http://dx.doi.org/10.1177/1473871616686634.

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The article describes a new unique method of multidimensional data visualization. It has been developed as modified observational tunnels method, which was previously known and used many times. The modification consists in supplementing the observational tunnels method used for visualization of multidimensional data with the concept of perspective. In this way, the orientation and navigation in multidimensional space are largely facilitated. The differences in effects of observational tunnels method and perspective-based observational tunnels method have been presented. The effectiveness of th

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