Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

Yanfeng Sun, Junbin Gao, Xia Hong, Bamdev Mishra, Baocai Yin

    Research output: Contribution to journalArticlepeer-review

    29 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number7182334
    Pages (from-to)476-489
    Number of pages14
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume38
    Issue number3
    Early online dateAug 2015
    DOIs
    Publication statusPublished - 01 Mar 2016

    Fingerprint

    Dive into the research topics of 'Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization'. Together they form a unique fingerprint.

    Cite this