Sparsiflcation of Probabilistic Canonical Correlation Analysis
Daniel Livingstone, Colin Fyfe · 2006
We have recently developed several ways of performing Canonical Correlation Analysis (1, 5, 7, 4) with probabilistic methods rather than the standard statistical tools. How- ever, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artiflcial neural network methods (3, 2). In this paper, we examine a recent development which sparsifles a probabilistic method of performing principal component analysis and then use this method to sparsify a new proba- bilistic method of performing canonical correlation analysis.