Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IDR/QR: an incremental dimension reduction algorithm via QR decomposition
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Online local learning algorithms for linear discriminant analysis
Pattern Recognition Letters - Special issue: Advances in pattern recognition
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Fast adaptive LDA using quasi-Newton algorithm
Pattern Recognition Letters
Fast algorithm for updating the discriminant vectors of dual-space LDA
IEEE Transactions on Information Forensics and Security
Algorithms and networks for accelerated convergence of adaptive LDA
Pattern Recognition
Adaptive algorithms and networks for optimal feature extraction from Gaussian data
Pattern Recognition Letters
Convergence proof of matrix dynamics for online linear discriminant analysis
Journal of Multivariate Analysis
An incremental subspace learning algorithm to categorize large scale text data
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
An incremental linear discriminant analysis using fixed point method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An unsupervised learning rule for class discrimination in a recurrent neural network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A new incremental optimal feature extraction method for on-line applications
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We describe self-organizing learning algorithms and associated neural networks to extract features that are effective for preserving class separability. As a first step, an adaptive algorithm for the computation of Q-1/2 (where Q is the correlation or covariance matrix of a random vector sequence) is described. Convergence of this algorithm with probability one is proven by using stochastic approximation theory, and a single-layer linear network architecture for this algorithm is described, which we call the Q-1/2 network. Using this network, we describe feature extraction architectures for: 1) unimodal and multicluster Gaussian data in the multiclass case; 2) multivariate linear discriminant analysis (LDA) in the multiclass case; and 3) Bhattacharyya distance measure for the two-class case. The LDA and Bhattacharyya distance features are extracted by concatenating the Q -1/2 network with a principal component analysis network, and the two-layer network is proven to converge with probability one. Every network discussed in the study considers a flow or sequence of inputs for training. Numerical studies on the performance of the networks for multiclass random data are presented