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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...
In multicamera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering. In this paper, a novel representation for multicamera video data, namely, the product Grassmann manifold (PGM), is proposed to model video sequences as points on the Grassmann manifold and integrate...
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