2018/11/01 by Yaqiang Yao, Yao, Yaqiang, Huanhuan Chen +1 · 1 citation
Computer Science · #Advanced Clustering Algorithms Research #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM
paper · pdf · doi:10.48550/arxiv.1811.00264
openalex publication_date 2018/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To cluster data that are not linearly separable in the original feature space, k-means clustering was extended to the kernel version. However, the performance of kernel k-means clustering largely depends on the choice of kernel function. To mitigate this problem, multiple kernel learning has been introduced into the k-means clustering to obtain an optimal kernel combination for clustering. Despite the success of multiple kernel k-means clustering in various scenarios, few of the existing work update the combination coefficients based on the diversity of kernels, which leads to the result that the selected kernels contain high redundancy and would degrade the clustering performance and efficiency. In this paper, we propose a simple but efficient strategy that selects a diverse subset from the pre-specified kernels as the representative kernels, and then incorporate the subset selection process into the framework of multiple k-means clustering. The representative kernels can be indicated as the significant combination weights. Due to the non-convexity of the obtained objective function, we develop an alternating minimization method to optimize the combination coefficients of the selected kernels and the cluster membership alternatively. We evaluate the proposed approach on several benchmark and real-world datasets. The experimental results demonstrate the competitiveness of our approach in comparison with the state-of-the-art methods.