Unsupervised learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Method Using Mutual Information and Hybrid Feature
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Discriminant independent component analysis
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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Low dimensional representation of multivariate data using unsupervised feature extraction is combined with a hybrid feature selection method to improve classification performance of recognition tasks. The proposed hybrid feature selector is applied to the union of feature subspaces selected by Fisher criterion and feature-class mutual information (MI). It scores each feature as a linear weighted sum of its interclass MI and Fisher criterion score. Proposed method efficiently selects features with higher class discrimination in comparison to feature-class MI, Fisher criterion or unsupervised selection using variance; thus, resulting in much improved recognition performance. In addition, the paper also highlights the use of MI between a feature and class as a computationally economical and optimal feature selector for statistically independent features.