PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiview Learning with Labels
Tom Diethe, David Hardoon and John Shawe-Taylor
In: Learning from Multiple Sources Workshop(2008).

Abstract

CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Discriminant Analysis uses label information to find informative projections, which can be more informative in supervised learning settings. We show that FDA and its dual can both be formulated as generalized eigenproblems, enabling a kernel formulation. We derive a regularised two-view equivalent of Fisher Discriminant Analysis and its corresponding dual, both of which can also be formulated as generalized eigenproblems. We then show that these can be cast as equivalent disciplined convex optimisation problems, and subsequently extended to multiple views. We show experimental results on an EEG dataset and part of the PASCAL 2007 VOC challenge dataset.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:4672
Deposited By:David Hardoon
Deposited On:13 March 2009