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Multiple kernel k-means clustering with matrix-induced regularization

Conference Paper


Abstract


  • © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Multiple kernel k-means (MKKM) clustering aims to optimally combine a group of pre-specified kernels to improve clustering performance. However, we observe that existing MKKM algorithms do not sufficiently consider the correlation among these kernels. This could result in selecting mutually redundant kernels and affect the diversity of information sources utilized for clustering, which finally hurts the clustering performance. To address this issue, this paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the selected kernels. We theoretically justify this matrix-induced regularization by revealing its connection with the commonly used kernel alignment criterion. Furthermore, this justification shows that maximizing the kernel alignment for clustering can be viewed as a special case of our approach and indicates the extendability of the proposed matrix-induced regularization for designing better clustering algorithms. As experimentally demonstrated on five challenging MKL benchmark data sets, our algorithm significantly improves existing MKKM and consistently outperforms the state-of-The-Art ones in the literature, verifying the effectiveness and advantages of incorporating the proposed matrix-induced regularization.

Authors


  •   Liu, Xinwang (external author)
  •   Dou, Yong (external author)
  •   Yin, Jianping (external author)
  •   Wang, Lei
  •   Zhu, En (external author)

Publication Date


  • 2016

Citation


  • Liu, X., Dou, Y., Yin, J., Wang, L. & Zhu, E. (2016). Multiple kernel k-means clustering with matrix-induced regularization. 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 1888-1894). United States: AAAI Press.

Scopus Eid


  • 2-s2.0-85007240963

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6508

Has Global Citation Frequency


Start Page


  • 1888

End Page


  • 1894

Place Of Publication


  • United States

Abstract


  • © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Multiple kernel k-means (MKKM) clustering aims to optimally combine a group of pre-specified kernels to improve clustering performance. However, we observe that existing MKKM algorithms do not sufficiently consider the correlation among these kernels. This could result in selecting mutually redundant kernels and affect the diversity of information sources utilized for clustering, which finally hurts the clustering performance. To address this issue, this paper proposes an MKKM clustering with a novel, effective matrix-induced regularization to reduce such redundancy and enhance the diversity of the selected kernels. We theoretically justify this matrix-induced regularization by revealing its connection with the commonly used kernel alignment criterion. Furthermore, this justification shows that maximizing the kernel alignment for clustering can be viewed as a special case of our approach and indicates the extendability of the proposed matrix-induced regularization for designing better clustering algorithms. As experimentally demonstrated on five challenging MKL benchmark data sets, our algorithm significantly improves existing MKKM and consistently outperforms the state-of-The-Art ones in the literature, verifying the effectiveness and advantages of incorporating the proposed matrix-induced regularization.

Authors


  •   Liu, Xinwang (external author)
  •   Dou, Yong (external author)
  •   Yin, Jianping (external author)
  •   Wang, Lei
  •   Zhu, En (external author)

Publication Date


  • 2016

Citation


  • Liu, X., Dou, Y., Yin, J., Wang, L. & Zhu, E. (2016). Multiple kernel k-means clustering with matrix-induced regularization. 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 1888-1894). United States: AAAI Press.

Scopus Eid


  • 2-s2.0-85007240963

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6508

Has Global Citation Frequency


Start Page


  • 1888

End Page


  • 1894

Place Of Publication


  • United States