A fast fixed-point algorithm for two-class discriminative feature extraction

Authors:
Zhirong Yang;Jorma Laaksonen
Affiliations:
Laboratory of Computer and Information Science, Helsinki University of Technology, HUT, Espoo, Finland;Laboratory of Computer and Information Science, Helsinki University of Technology, HUT, Espoo, Finland
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

We propose a fast fixed-point algorithm to improve the Relevant Component Analysis (RCA) in two-class cases. Using an objective function that maximizes the predictive information, our method is able to extract more than one discriminative component of data for two-class problems, which cannot be accomplished by classical Fisher's discriminant analysis. After prewhitening the data, we apply Newton's optimization method which automatically chooses the learning rate in the iterative training of each component. The convergence of the iterative learning is quadratic, i.e. much faster than the linear optimization by gradient methods. Empirical tests presented in the paper show that feature extraction using the new method resembles RCA for low-dimensional ionosphere data and significantly outperforms the latter in efficiency for high-dimensional facial image data.