Canonical Correlation Analysis for Multiview Semisupervised Feature Extraction

  • Olcay Kursun
  • Ethem Alpaydin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6113)

Abstract

Hotelling’s Canonical Correlation Analysis (CCA) works with two sets of related variables, also called views, and its goal is to find their linear projections with maximal mutual correlation. CCA is most suitable for unsupervised feature extraction when given two views but it has been also long known that in supervised learning when there is only a single view of data given, the supervision signal (class-labels) can be given to CCA as the second view and CCA simply reduces to Fisher’s Linear Discriminant Analysis (LDA). However, it is unclear how to use this equivalence for extracting features from multiview data in semisupervised setting (i.e. what modification to the CCA mechanism could incorporate the class-labels along with the two views of the data when labels of some samples are unknown). In this paper, a CCA-based method supplemented by the essence of LDA is proposed for semi-supervised feature extraction from multiview data.

Keywords

Semisupervised Learning Feature Extraction Multiview Learning LDA CCA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Olcay Kursun
    • 1
  • Ethem Alpaydin
    • 2
  1. 1.Department of Computer EngineeringIstanbul UniversityAvcilarTurkey
  2. 2.Department of Computer EngineeringBogazici UniversityBebekTurkey

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