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Heterogeneous tensor decomposition for clustering via manifold optimization

Sun, Y., Gao, J., Hong, X., Mishra, B. and Yin, B. (2016) Heterogeneous tensor decomposition for clustering via manifold optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (3). pp. 476-489. ISSN 0162-8828

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To link to this item DOI: 10.1109/TPAMI.2015.2465901

Abstract/Summary

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:65629
Publisher:IEEE

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