PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Two view learning: SVM-2K, Theory and Practice
Jason Farquhar, David Hardoon, Hongying Meng, John Shawe-Taylor and Sandor Szedmak
In: NIPS 2005, 5 Dec - 8 Dec 2005, Vancouver.


Kernel methods make it relatively easy to define complex high-dimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). This paper takes this observation to its logical conclusion and proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K. We present both experimental and theoretical analysis of the approach showing encouraging results and insights.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1509
Deposited By:Sandor Szedmak
Deposited On:28 November 2005