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This paper explores the use of Two-Dimensional Robust Neighborhood Discriminant Embedding (2D-RNDE) as a means to improve the performance and robustness of face recognition. 2D-RNDE is based on graph embedding framework and Fisher's criterion, it can utilize the original two-dimensional image data directly and takes into account the Individual Discriminative Factor (IDF) which is proposed to describe...
Principal Component Analysis (PCA) is a wellknown and efficient technique for feature extraction and dimension reduction, which has been applied widely in community of machine learning and pattern recognition. But traditional PCA suffers from two disadvantages which restricts it's treatment of two dimensional data, like human faces, fingerprints, palmprints and other biological features which are...
One of the challenges the face recognition application is facing today is that of the high dimensionality of multivariate data. In this context, this paper proposes to compare the performance of a triumvirate combination of linear dimensionality reduction techniques namely Singular Value Decomposition (SVD) which maximizes the variance of the training vectors, Direct Fractional Linear Discriminant...
Principal components analysis (PCA) and linear discriminant analysis (LDA) are the two popular techniques in the context of dimensionality reduction and classification. By extracting discriminant features, LDA is optimal when the distributions of the features for each class are unimodal and separated by the scatter of means. On the other hand, PCA extract descriptive features which helps itself to...
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional...
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