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Face recognition (FR) has received significant attention as one of the most successful applications of image analysis and understanding, during the past several years and is an active yet challenging topic in computer vision applications. Also potentially will help in identifying ultra-rare and developmental disorders. Linear discriminant analysis (LDA) has been widely used for feature extraction...
Neighborhood preserving embedding (NPE) is a typical graph-based dimensionality reduction algorithm, which has been successfully applied in many practical problems such as face representation and recognition. NPE depends mainly on its underlying graph matrix which characters the local neighborhood reconstruction relationship between data points. However, the graph constructed in NPE merely utilizes...
Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to...
In this paper, we extend the original projective non-negative matrix factorization (P-NMF) to kernel P-NMF (KP-NMF). The advantages of KP-NMF over P-NMF are:1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with non-linear data well; 3) it can process data with negative values by using some specific kernel functions...
Most manifold learning based methods preserve the original neighbor relationships to pursue the discriminating power. Thus, structure information of data distribution might be neglected and destroyed in low-dimensional space in a sense. In this paper, a novel supervised method, called Locality Preserving Embedding (LPE), is proposed to feature extraction and dimensionality reduction. LPE gives a low-dimensional...
This paper addresses the problem of representing multimedia information under a compressed form that permits efficient classification. The semantic coding problem starts from a subspace method where dimensionality reduction is formulated as a matrix factorization problem. Data samples are jointly represented in a common subspace extracted from a redundant dictionary of basis functions. We first build...
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