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Multiple kernel k-means with incomplete kermels

Conference Paper


Abstract


  • Multiple kernel clustering (MKC) algorithms optimally combine

    a group of pre-specified base kernels to improve clustering

    performance. However, existing MKC algorithms cannot

    efficiently address the situation where some rows and

    columns of base kernels are absent. This paper proposes a

    simple while effective algorithm to address this issue. Different

    from existing approaches where incomplete kernels are

    firstly imputed and a standard MKC algorithm is applied to

    the imputed kernels, our algorithm integrates imputation and

    clustering into a unified learning procedure. Specifically, we

    perform multiple kernel clustering directly with the presence

    of incomplete kernels, which are treated as auxiliary variables

    to be jointly optimized. Our algorithm does not require that

    there be at least one complete base kernel over all the samples.

    Also, it adaptively imputes incomplete kernels and combines

    them to best serve clustering. A three-step iterative algorithm

    with proved convergence is designed to solve the resultant

    optimization problem. Extensive experiments are conducted

    on four benchmark data sets to compare the proposed

    algorithm with existing imputation-based methods. Our algorithm

    consistently achieves superior performance and the improvement

    becomes more significant with increasing missing

    ratio, verifying the effectiveness and advantages of the proposed

    joint imputation and clustering.

Authors


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

Publication Date


  • 2017

Citation


  • Liu, X., Li, M., Wang, L., Dou, Y., Yin, J. & Zhu, E. (2017). Multiple kernel k-means with incomplete kermels. 31st AAAI Conference (AAAI 2017) (pp. 1-7). United States: Association for the Advancement of Artificial Intelligence.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6480

Start Page


  • 1

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  • 7

Abstract


  • Multiple kernel clustering (MKC) algorithms optimally combine

    a group of pre-specified base kernels to improve clustering

    performance. However, existing MKC algorithms cannot

    efficiently address the situation where some rows and

    columns of base kernels are absent. This paper proposes a

    simple while effective algorithm to address this issue. Different

    from existing approaches where incomplete kernels are

    firstly imputed and a standard MKC algorithm is applied to

    the imputed kernels, our algorithm integrates imputation and

    clustering into a unified learning procedure. Specifically, we

    perform multiple kernel clustering directly with the presence

    of incomplete kernels, which are treated as auxiliary variables

    to be jointly optimized. Our algorithm does not require that

    there be at least one complete base kernel over all the samples.

    Also, it adaptively imputes incomplete kernels and combines

    them to best serve clustering. A three-step iterative algorithm

    with proved convergence is designed to solve the resultant

    optimization problem. Extensive experiments are conducted

    on four benchmark data sets to compare the proposed

    algorithm with existing imputation-based methods. Our algorithm

    consistently achieves superior performance and the improvement

    becomes more significant with increasing missing

    ratio, verifying the effectiveness and advantages of the proposed

    joint imputation and clustering.

Authors


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

Publication Date


  • 2017

Citation


  • Liu, X., Li, M., Wang, L., Dou, Y., Yin, J. & Zhu, E. (2017). Multiple kernel k-means with incomplete kermels. 31st AAAI Conference (AAAI 2017) (pp. 1-7). United States: Association for the Advancement of Artificial Intelligence.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6480

Start Page


  • 1

End Page


  • 7