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Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning

  • Christoph H. Lampert
  • Oliver Krömer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.

Keywords

Dimensionality Reduction Linear Discriminant Analysis Local Binary Pattern Canonical Correlation Analysis Transfer Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christoph H. Lampert
    • 1
  • Oliver Krömer
    • 2
  1. 1.Institute of Science and Technology AustriaKlosterneuburgAustria
  2. 2.Max Planck Institute for Biological CyberneticsTübingenGermany

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