Local sparse representation and discriminant analysis for feature extraction
Songjiang Lou, Zhao Xaio-ming · Journal of Optoelectronics·laser · 2013
It is important to extract discriminant features for pattern recognition.Manifold learning can deal with the nonlinearity hidden in the data and the sparse representation shows its robustness.To extract discriminant and robust features,in this paper,a method called local sparse representation and discriminant analysis is proposed,which preserves the local sparse relationship and maximizes the inter-class separatability.As a result,the extracted features are sparse,discriminative and helpful for classification. Experiments on two open face databases,ORL and Yale,show that the proposed algorithm improves the accuracy,and the correctness and effectiveness of the proposed algorithm are confirmed.