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Fast adaptive algorithms and networks for class-separability features
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DOI：10.1016/S0031-3203(03)00006-2.png)
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In this article, we introduce accelerated algorithms for. linear discriminant analysis (LDA) and feature extraction from unimodal multiclass Gaussian data. Current adaptive methods based on the gradient descent optimization technique use a fixed or a monotonically decreasing step size in each iteration, which results in a slow convergence rate. Here, we use a variable step size, optimally computed in each iteration using the steepest descent method, in order to accelerate the convergence of the algorithm. Based on the new adaptive algorithm, we present a self-organizing neural network for adaptive computation of the square root of the inverse covariance matrix (Sigma(-1/2)) and use it (i) in a network for optimal feature extraction from Gaussian data and (ii) in cascaded form with a principal component analysis network for LDA. Experimental results demonstrate fast convergence and high stability of the algorithm and justify its advantages for on-line pattern recognition applications with stationary and non-stationary input data. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
linear discriminant analysis
principal component analysis
feature extraction
gradient descent optimization
steepest descent optimization
self-organizing neural network
adaptive algorithms
convergence analysis
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被引数:
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