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Sparse representation has been extensively studied in the signal processing community, which shows that one target sample can be accurately recovered by a sparse linear combination of the overall data. Such discovery has soon been applied to the pattern recognition task and, more recently, given rise to two new feature extraction methods, namely sparsity preserving projections (SPP) and global sparse...
Sparse representation has been extensively studied in the signal processing community, which surprisingly pointed out that one target signal can be accurately represented as a linear combination of very few measurement signals, often called atoms, in a given dictionary. This discovery has soon been employed to the field of pattern recognition and more recently, given rise to a newly developed unsupervised...
A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and discriminant vectors, then we use 2D features to perform the Kernel discriminant analysis. The nearest neighbor classifier with...
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