Supervised multiple kernel embedding for learning predictive subspaces

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

For supervised learning problems, dimensionality reduction is generally applied as a preprocessing step. However, coupled training of dimensionality reduction and supervised learning steps may improve the prediction performance. In this paper, we propose a novel dimensionality reduction algorithm coupled with a supervised kernel-based learner, called supervised multiple kernel embedding, that integrates multiple kernel learning to dimensionality reduction and performs prediction on the projected subspace with a joint optimization framework. Combining multiple kernels allows us to combine different feature representations and/or similarity measures toward a unified subspace. We perform experiments on one digit recognition and two bioinformatics data sets. Our proposed method significantly outperforms multiple kernel Fisher discriminant analysis followed by a standard kernel-based learner, especially on low dimensions.

Original languageEnglish (US)
Article number6338928
Pages (from-to)2381-2389
Number of pages9
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number10
DOIs
StatePublished - Sep 5 2013

Keywords

  • Dimensionality reduction
  • kernel machines
  • multiple kernel learning
  • subspace learning
  • supervised learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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