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Recently there has been increasing interest in deep neural network due to its powerful represent ability in several successful applications such as speech recognition and image classification. In this paper, we propose a general nonlinear embedding framework based on deep neural network which can be utilized to implement a family of dimensionality reduction algorithms. The objective function of our...
Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
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