Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Discriminative Features for Document Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Discriminant Analysis: Feature Extraction with an Information-Theoretic Objective
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probability of error, equivocation, and the Chernoff bound
IEEE Transactions on Information Theory
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
A Discriminant Analysis Method for Face Recognition in Heteroscedastic Distributions
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Regularized discriminant entropy analysis
Pattern Recognition
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In this article we develop a novel linear dimensionality reduction technique for classification. The technique utilizes the first two statistical moments of data and retains the computational simplicity, characteristic of second-order techniques, such as linear discriminant analysis. Formally, the technique maximizes a criterion that belongs to the class of probability dependence measures, and is naturally defined for multiple classes. The criterion is based on an approximation of an information-theoretic measure and is capable of handling heteroscedastic data. The performance of our method, along with similar feature extraction approaches, is demonstrated based on experimental results with real-world datasets. Our method compares favorably to similar second-order linear dimensionality techniques.