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- W3091857251 abstract "Multiview subspace clustering has attracted an increasing amount of attention in recent years. However, most of the existing multiview subspace clustering methods assume linear relations between multiview data points when learning the affinity representation by means of the self-expression or fail to preserve the locality property of the original feature space in the learned affinity representation. To address the above issues, in this article, we propose a new multiview subspace clustering method termed smoothness regularized multiview subspace clustering with kernel learning (SMSCK). To capture the nonlinear relations between multiview data points, the proposed model maps the concatenated multiview observations into a high-dimensional kernel space, in which the linear relations reflect the nonlinear relations between multiview data points in the original space. In addition, to explicitly preserve the locality property of the original feature space in the learned affinity representation, the smoothness regularization is deployed in the subspace learning in the kernel space. Theoretical analysis has been provided to ensure that the optimal solution of the proposed model meets the grouping effect. The unique optimal solution of the proposed model can be obtained by an optimization strategy and the theoretical convergence analysis is also conducted. Extensive experiments are conducted on both image and document data sets, and the comparison results with state-of-the-art methods demonstrate the effectiveness of our method." @default.
- W3091857251 created "2020-10-15" @default.
- W3091857251 creator A5034685928 @default.
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- W3091857251 date "2021-11-01" @default.
- W3091857251 modified "2023-10-18" @default.
- W3091857251 title "Smoothness Regularized Multiview Subspace Clustering With Kernel Learning" @default.
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- W3091857251 doi "https://doi.org/10.1109/tnnls.2020.3026686" @default.
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- W3091857251 hasPublicationYear "2021" @default.
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