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The PCA dimensionality reduction algorithm for 2D data with the Laplacian noise model, i.e., L1-2DPCA, not only preserves the structural relation among 2D data, but also is robust for data outliers. The algorithm relies on the EM algorithm with great computational cost. In order to learn intrinsic information more consistently, this paper takes a view of manifold optimization for the model based on...
Many application scenarios involve sequential data, but most existing clustering methods do not well utilize the order information embedded in sequential data. In this paper, we study the subspace clustering problem for sequential data and propose a new clustering method, namely ordered sparse clustering with block-diagonal prior (BD-OSC). Instead of using the sparse normalizer in existing sparse...
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