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Overview: A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. This research paper propose a new algorithm, called Concurrent Subspaces Analysis (CSA), to derive representative subspaces by encoding image objects as 2nd or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively.
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