Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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 covariance estimate for Bayesian classifiers in biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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We present an enhanced direct linear discriminant analysis (EDLDA) solution to effectively and efficiently extract discriminatory features from high dimensional data. The EDLDA integrates two types of class-wise weighting terms in estimating the average within-class and between-class scatter matrices in order to relate the resulting Fisher criterion more closely to the minimization of classification error. Furthermore, the extracted discriminant features are weighted by mutual information between features and class labels. Experimental results on four biometric datasets demonstrate the promising performance of the proposed method.