${l_{2,1}}$ norm regularization term to realize supervised sparse constraint for the representation learning. Next, the low-rank constraint is used to capture the inherent global structure information of data. Finally, we introduce a classification loss term with transformation matrix for joint optimization, such that the projection learning is not limited to number of classes, and the discriminative ability is further improved. Comprehensive experimental results on six benchmarks verify that our method achieves promising performance with other state-of-the-arts in both robustness and effectiveness.">

Fuzzy Discriminative Block Representation Learning for Image Feature Extraction (original) (raw)

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