Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In a previous paper [8] we have proposed a method to improve the classification between two classes in a new transformed space using the Chernoff similarity measure. The key idea is to estimate a transformation matrix such that the overlap between the pdf associated to the competing classes is minimum thus leading to a minimization of the classification error. Starting from a surrogate cost function we review the previous method from the consideration that in many practical applications the (online) learning examples come in a sample-by-sample manner instead of a batch manner. Then we propose a new formulation of the learning algorithm in on-line mode and we derive the corresponding formulation. We arrive to iterative formulations of the estimation processes. The classes are modeled by a Gaussian mixture model with a varying number of components and we investigate the new method for several dimensionalities of the transformed subspace. The experiments are carried out over a database of speech with and without pathology and we show that the performance of the on-line approach compares favorably with respect to the batch mode and outperforms some reference methods.