Adaptive feature transformation for classification with sparse representation

Abstract Sparse representation based classification (SRC) has been shown to be an effective method for face recognition. Furthermore, the input features of more and more classifiers are extracted by dimensional reduction methods. However, we find that the reconstruction ability of a basis for a testing sample is related with cosine distance between this basis and this testing sample, but most dimensional reduction methods are based on Euclidean distance. Obviously, a gap is existing between dimensional reduction methods and SRC. In this paper, we propose an adaptive feature transformation based on self-tuning point to point distances (SPPDAFT) to transform features to a new feature space. The cosine distances among samples in new feature space that is obtainted by SPPDAFT can increase with the Euclidean distances among samples in original space. As a result, the reconstruction ability of a basis to a testing sample in new feature space would be indirectly related with the Euclidean distance between this basis and this sample in original space, and then the gap between dimensional reduction methods and SRC can be reduced. The experimental results on benchmark databases show the effectiveness of SPPDAFT.

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