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Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices

Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices We consider the problem of combining multiple dissimilarity representations via the Cartesian product of their embeddings. For concreteness, we choose the inferential task at hand to be classification. The high dimensionality of this Cartesian product space implies the necessity of dimensionality reduction before training a classifier. We propose a supervised dimensionality reduction method, which utilizes the class label information, to help achieve a favorable combination. The simulation and real data results show that our approach can improve classification accuracy compared to the alternatives of principal components analysis and no dimensionality reduction at all. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Dimensionality Reduction on the Cartesian Product of Embeddings of Multiple Dissimilarity Matrices

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References (36)

Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Statistics; Marketing ; Psychometrics; Signal, Image and Speech Processing; Bioinformatics; Pattern Recognition; Statistical Theory and Methods
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-010-9059-3
Publisher site
See Article on Publisher Site

Abstract

We consider the problem of combining multiple dissimilarity representations via the Cartesian product of their embeddings. For concreteness, we choose the inferential task at hand to be classification. The high dimensionality of this Cartesian product space implies the necessity of dimensionality reduction before training a classifier. We propose a supervised dimensionality reduction method, which utilizes the class label information, to help achieve a favorable combination. The simulation and real data results show that our approach can improve classification accuracy compared to the alternatives of principal components analysis and no dimensionality reduction at all.

Journal

Journal of Classification – Springer Journals

Published: Oct 8, 2010

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