Two methods for sparsifying probabilistic canonical correlation analysis

Authors:
Colin Fyfe;Gayle Leen
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Scotland;Applied Computational Intelligence Research Unit, The University of Paisley, Scotland
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

We have recently developed several ways of performing Canonical Correlation Analysis [1,5,7,4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3,2]. In this paper, we examine two recent developments which sparsify probabilistic methods of performing canonical correlation analysis.