IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v17y2002i2d10.1007_s001800200099.html
   My bibliography  Save this article

Extensions of Classical Multidimensional Scaling via Variable Reduction

Author

Listed:
  • Michael W. Trosset

    (College of William & Mary)

Abstract

Summary Classical multidimensional scaling constructs a configuration of points that minimizes a certain measure of discrepancy between the configuration’s interpoint distance matrix and a fixed dissimilarity matrix. Recent extensions have replaced the fixed dissimilarity matrix with a closed and convex set of dissimilarity matrices. These formulations replace fixed dissimilarities with optimization variables (disparities) that are permitted to vary subject to application-specific constraints. For example, simple bound constraints are suitable for distance matrix completion problems (Trosset, 2000) and for inferring molecular conformation from information about interatomic distances (Trosset, 1998b); whereas order constraints are suitable for nonmetric multidimensional scaling (Trosset, 1998a). This paper describes the computational theory that provides a common foundation for these formulations.

Suggested Citation

  • Michael W. Trosset, 2002. "Extensions of Classical Multidimensional Scaling via Variable Reduction," Computational Statistics, Springer, vol. 17(2), pages 147-163, July.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200099
    DOI: 10.1007/s001800200099
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s001800200099
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s001800200099?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gale Young & A. Householder, 1938. "Discussion of a set of points in terms of their mutual distances," Psychometrika, Springer;The Psychometric Society, vol. 3(1), pages 19-22, March.
    2. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    3. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    4. Takayuki Saito, 1978. "The problem of the additive constant and eigenvalues in metric multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 43(2), pages 193-201, June.
    5. A. S. Lewis, 1996. "Derivatives of Spectral Functions," Mathematics of Operations Research, INFORMS, vol. 21(3), pages 576-588, August.
    6. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Malone, Samuel W. & Tarazaga, Pablo & Trosset, Michael W., 2002. "Better initial configurations for metric multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 143-156, November.
    2. Lewis, R.M. & Trosset, M.W., 2006. "Sensitivity analysis of the strain criterion for multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 135-153, January.
    3. Herden, Gerhard & Pallack, Andreas, 2005. "Adequateness and interpretability of objective functions in ordinal data analysis," Journal of Multivariate Analysis, Elsevier, vol. 94(1), pages 19-69, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Si-Tong Lu & Miao Zhang & Qing-Na Li, 2020. "Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints," Computational Optimization and Applications, Springer, vol. 76(2), pages 535-569, June.
    2. J. Carroll, 1985. "Review," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 133-140, March.
    3. Aurea Grané & Rosario Romera, 2018. "On Visualizing Mixed-Type Data," Sociological Methods & Research, , vol. 47(2), pages 207-239, March.
    4. Flora Josiane Chadare & Nadia Fanou Fogny & Yann Eméric Madode & Juvencio Odilon G. Ayosso & Sèwanou Hermann Honfo & Folachodé Pierre Polycarpe Kayodé & Anita Rachel Linnemann & Djidjoho Joseph Hounho, 2018. "Local agro-ecological condition-based food resources to promote infant food security: a case study from Benin," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(4), pages 1013-1031, August.
    5. Jacqueline Meulman, 1992. "The integration of multidimensional scaling and multivariate analysis with optimal transformations," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 539-565, December.
    6. Daniel B. McArtor & Gitta H. Lubke & C. S. Bergeman, 2017. "Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1052-1077, December.
    7. Herden, Gerhard & Pallack, Andreas, 2002. "Consistency in ordinal data analysis I," Mathematical Social Sciences, Elsevier, vol. 43(1), pages 79-113, January.
    8. Akinori Okada & Tadashi Imaizumi, 1997. "Asymmetric multidimensional scaling of two-mode three-way proximities," Journal of Classification, Springer;The Classification Society, vol. 14(2), pages 195-224, September.
    9. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    10. Venera Tomaselli, 1996. "Multivariate statistical techniques and sociological research," Quality & Quantity: International Journal of Methodology, Springer, vol. 30(3), pages 253-276, August.
    11. Simensen, Trond & Halvorsen, Rune & Erikstad, Lars, 2018. "Methods for landscape characterisation and mapping: A systematic review," Land Use Policy, Elsevier, vol. 75(C), pages 557-569.
    12. Bijmolt, T.H.A. & Wedel, M., 1996. "A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods," Other publications TiSEM f72cc9d8-f370-43aa-a224-4, Tilburg University, School of Economics and Management.
    13. Willem Heiser, 1991. "A generalized majorization method for least souares multidimensional scaling of pseudodistances that may be negative," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 7-27, March.
    14. Lewis, R.M. & Trosset, M.W., 2006. "Sensitivity analysis of the strain criterion for multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 135-153, January.
    15. Luís Francisco Aguiar & Pedro C. Magalhães & Maria Joana Soares, 2010. "Synchronism in Electoral Cycles: How United are the United States?," NIPE Working Papers 17/2010, NIPE - Universidade do Minho.
    16. Morales José F. & Song Tingting & Auerbach Arleen D. & Wittkowski Knut M., 2008. "Phenotyping Genetic Diseases Using an Extension of µ-Scores for Multivariate Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-20, June.
    17. Kennen, Jonathan G. & Kauffman, Leon J. & Ayers, Mark A. & Wolock, David M. & Colarullo, Susan J., 2008. "Use of an integrated flow model to estimate ecologically relevant hydrologic characteristics at stream biomonitoring sites," Ecological Modelling, Elsevier, vol. 211(1), pages 57-76.
    18. José Luis Ortega Priego, 2003. "A Vector Space Model as a methodological approach to the Triple Helix dimensionality: A comparative study of Biology and Biomedicine Centres of two European National Research Councils from a Webometri," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 429-443, October.
    19. Keith Poole, 1990. "Least squares metric, unidimensional scaling of multivariate linear models," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 123-149, March.
    20. Berrie Zielman & Willem Heiser, 1993. "Analysis of asymmetry by a slide-vector," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 101-114, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200099. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.