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Laplacian PCA tries to maximize the intra-class covariance across all samples while preserving the local manifold information.However, the static geometry center is incompetent to express the data's manifold structure, and easily influenced by the unique noise point. Moreover, Laplacian PCA also neglects the upgraded information in the objective space. In this paper, we propose the Dynamic Laplacian...
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