Abstract：The original dataset is decomposed into a set of representative bases with corresponding sparse errors for modeling the raw data in standard low-rank matrix recovery algorithm. Inspired by the Fisher criterion, a low-rank matrix recovery algorithm based on Fisher discriminate criterion is presented in this paper. Low-rank matrix recovery is executed in a supervised learning mode, i.e., taking the within-class scatter and between-class scatter into account when the whole label information is available. The proposed model can be solved by the augmented Lagrange multipliers, and the additional discriminating ability is provided to the standard low-rank models for improving performance. The representative bases learned by the proposed algorithm are encouraged to be structurally coherent within the same class and be independent between classes as much as possible. Numerical simulations on face recognition tasks demonstrate that the proposed algorithm is competitive with the state-of-the-art alternatives.
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2015, Vol. 28 
