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Often, a data object described by many features can be decomposed as multi-modalities, which always provide complementary information to each other. In this paper, we study subspace clustering for multi-modal data by effectively exploiting data correlation consensus across modalities, while keeping individual modalities well encapsulated. Our technique can yield a more ideal data similarity matrix, which encodes strong data correlations for the cross-modal data objects in the same subspace.
Wang et al. (Mon,) studied this question.
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