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In this paper, we propose an effective supervised dimensionality reduction technique, namely discrimi-nant sparsity neighborhood preserving embedding (DSNPE), for face recognition. DSNPE constructs graph and corresponding edge weights simultaneously through sparse representation (SR). DSNPE explicitly takes into account the within-neighboring information(More)
Dimensionality reduction has become an important data preprocessing step in a lot of applications. Linear discriminant analysis (LDA) is one of the most well-known dimensionality reduction methods. However, the classical LDA cannot be used directly in the small sample size (SSS) problem where the within-class scatter matrix is singular. In the past, many(More)
In this article, the kernel-based methods explained by a graph embedding framework are analyzed and their nature is revealed, i.e. any kernel-based method in a graph embedding framework is equivalent to kernel principal component analysis plus its corresponding linear one. Based on this result, the authors propose a complete kernel-based algorithms(More)
Null space based linear discriminant analysis (NSLDA) is a well-known feature extraction method, which can make use of the most discriminant information in the null space of within-class scatter matrix. However, the conventional formulation of NSLDA is based on L2-norm which makes NSLDA be sensitive to outlier. To address the problem of NSLDA, in this(More)