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Semisupervised learning from dissimilarity data

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  • Trosset, Michael W.
  • Priebe, Carey E.
  • Park, Youngser
  • Miller, Michael I.

Abstract

The following two-stage approach to learning from dissimilarity data is described: (1) embed both labeled and unlabeled objects in a Euclidean space; then (2) train a classifier on the labeled objects. The use of linear discriminant analysis for (2), which naturally invites the use of classical multidimensional scaling for (1), is emphasized. The choice of the dimension of the Euclidean space in (1) is a model selection problem; too few or too many dimensions can degrade classifier performance. The question of how the inclusion of unlabeled objects in (1) affects classifier performance is investigated. In the case of spherical covariances, including unlabeled objects in (1) is demonstrably superior. Several examples are presented.

Suggested Citation

  • Trosset, Michael W. & Priebe, Carey E. & Park, Youngser & Miller, Michael I., 2008. "Semisupervised learning from dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4643-4657, June.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:10:p:4643-4657
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    1. Trosset, Michael W. & Priebe, Carey E., 2008. "The out-of-sample problem for classical multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4635-4642, June.
    2. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
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    1. Trosset, Michael W. & Priebe, Carey E., 2008. "The out-of-sample problem for classical multidimensional scaling," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4635-4642, June.

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