Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Approximation Method of the Quadratic Discriminant Function
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
IEICE - Transactions on Information and Systems
SVM decision boundary based discriminative subspace induction
Pattern Recognition
The bayes-optimal feature extraction procedure for pattern recognition using genetic algorithm
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A new multidimensional feature transformation for linear classifiers and its applications
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Dimension reduction is the process of transforming multidimensional vectors into a low-dimensional space. In pattern recognition, it is often desired that this task be performed without significant loss of classification information. The Bayes error is an ideal criterion for this purpose; however, it is known to be notoriously difficult for mathematical treatment. Consequently, suboptimal criteria have been used in practice. We propose an alternative criterion, based on the estimate of the Bayes error, that is hopefully closer to the optimal criterion than the criteria currently in use. An algorithm for linear dimension reduction, based on this criterion, is conceived and implemented. Experiments demonstrate its superior performance in comparison with conventional algorithms.