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- W2016758891 abstract "Classification performance of recognition tasks can be improved by selection of highly discriminative featuresfrom the low-dimensional linear representation of data. High-dimensional multivariate data can be representedin lower dimensions by unsupervised feature extraction techniques which attempts to remove the redundancyin the data and/or resolve the multivariate prediction problems. These extracted low-dimensional features ofraw data may not ensure good class discrimination, therefore, supervised feature selection methods motivatedby information-theoretic approaches can improve the recognition performance with lesser number of features.Proposed hybrid feature selection methods efficiently selects features with higher class discrimination in comparisonto feature-class mutual information (MI), Fisher criterion or unsupervised selection using variance; thus, resulting in much improved recognition performance. Feature-class MI criterion and hybrid feature selection methods are computationally scalable and optimal selectors for statistically independent features." @default.
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- W2016758891 modified "2023-09-23" @default.
- W2016758891 title "Information-theoretic feature extraction and selection for robust classification" @default.
- W2016758891 doi "https://doi.org/10.1117/12.822569" @default.
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