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Tensor-based feature extraction approaches have been proved to be effective since they can solve the undersampled problem. In this paper, we propose a novel method called projection-optimal tensor local fisher discriminant analysis (PoTLFDA), which shares the character of local fisher discriminant analysis (LFDA). A novel affinity matrix is defined to effectively reflect the relationships of points...
In recent years, feature extraction methods make an achievement in pattern recognition and computer vision. It extracts not only useful feature for classification, but also reduces the dimension of pattern samples. In this paper, we propose orthogonal supervised spectral discriminant analysis (OSSDA) which motivated by marginal fisher analysis (MFA) and spectral clustering. It put different weights...
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