Unsupervised non-parametric kernel learning algorithm
A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsuper...
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| Vydáno v: | Knowledge-based systems Ročník 44; s. 1 - 9 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.05.2013
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold Regularized Least-Squares (RLS) to learn non-parametric kernels efficiently. The proposed algorithm enjoys a closed-form solution in each iteration, which can be efficiently computed by the Lanczos sparse eigen-decomposition technique. Meanwhile, it can be extended to supervised kernel learning naturally. Experimental results show that our proposed unsupervised non-parametric kernel learning algorithm is significantly more effective and applicable to enhance the performance of Maximum Margin Clustering (MMC). Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings. |
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| AbstractList | A foundational problem in kernel-based learning is how to design suitable kernels. Non-Parametric Kernel Learning (NPKL) is one of the most important kernel learning methods. However, most research on NPKL has tended to focus on the semi-supervised scenario. In this paper, we propose a novel unsupervised non-parametric kernel learning method, which can seamlessly combine the spectral embedding of unlabeled data and manifold Regularized Least-Squares (RLS) to learn non-parametric kernels efficiently. The proposed algorithm enjoys a closed-form solution in each iteration, which can be efficiently computed by the Lanczos sparse eigen-decomposition technique. Meanwhile, it can be extended to supervised kernel learning naturally. Experimental results show that our proposed unsupervised non-parametric kernel learning algorithm is significantly more effective and applicable to enhance the performance of Maximum Margin Clustering (MMC). Especially, it outperforms multiple kernel learning in both unsupervised and supervised settings. |
| Author | Liu, Bing Xia, Shi-Xiong Zhou, Yong |
| Author_xml | – sequence: 1 givenname: Bing surname: Liu fullname: Liu, Bing – sequence: 2 givenname: Shi-Xiong surname: Xia fullname: Xia, Shi-Xiong email: xiasx@cumt.edu.cn – sequence: 3 givenname: Yong surname: Zhou fullname: Zhou, Yong |
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| Cites_doi | 10.1145/1015330.1015424 10.1145/1553374.1553537 10.1145/1150402.1150426 10.1109/ICCV.2009.5459183 10.1016/j.knosys.2012.05.016 10.1109/TSMCB.2011.2168604 10.1016/j.knosys.2009.06.009 10.1145/1143844.1143908 10.1145/1273496.1273542 10.1145/1150402.1150429 10.1145/1553374.1553510 10.1109/TNN.2009.2036998 10.1109/TNN.2008.2010620 10.1109/CVPR.2009.5206721 10.1145/1143844.1143857 10.1109/CVPR.2009.5206747 10.1109/MLSP.2008.4685446 10.1145/1102351.1102484 10.1137/1.9781611972795.55 |
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| Keywords | Multiple kernel learning Manifold regularized least-squares Maximum margin clustering (MMC) Non-parametric kernel learning Sparse eigen-decomposition |
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| SubjectTerms | Algorithms Clustering Data Exact solutions Kernels Knowledge base Learning Least squares method Machine learning Manifold regularized least-squares Mathematical analysis Maximum margin clustering (MMC) Multiple kernel learning Non-parametric kernel learning Sparse eigen-decomposition Spectra |
| Title | Unsupervised non-parametric kernel learning algorithm |
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