Joint diversity regularization and graph regularization for multiple kernel k-means clustering via latent variables

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• 作者：Teng Li ; ^{liteng09@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yong Dou ^{yongdou@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xinwang Liu ^{1022xinwang.liu@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Clustering ; Kernel k-means ; Latent variables ; Diversity regularization ; Graph regularization
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 December 2016
• 年：2016
• 卷：218
• 期：Complete
• 页码：154-163
• 全文大小：1640 K

文摘

Multiple kernel k-means (MKKM) clustering algorithm is widely used in many machine learning and computer vision tasks. This algorithm improves clustering performance by extending the traditional kernel k-means (KKM) clustering algorithm to a multiple setting by combining a group of pre-specified kernels. In this paper, we develop and propose a multiple kernel k-means clustering via latent variables (MKKLV) algorithm, in which base kernels can be adaptively adjusted with respect to each sample. To improve the effectiveness of the kernel-specific and sample-specific characteristics of the data, joint diversity regularization and graph regularization are utilized in the MKKLV algorithm. An efficient three-step iterative algorithm is employed to jointly optimize the kernel-specific and sample-specific coefficients. Experiments validate that our algorithm outperforms state-of-the-art techniques on several different benchmark datasets.