# Multiple Kernel k-Means Clustering by Selecting Representative Kernels

arXiv: https://arxiv.org/abs/1811.00264 · HTML: https://mlsanity.com/paper/1811.00264/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning](https://mlsanity.com/paper/2103.17086/) (2021)
- [Multi-View Class Incremental Learning](https://mlsanity.com/paper/2306.09675/) (2023)
- [Contrastive Continual Multi-view Clustering with Filtered Structural Fusion](https://mlsanity.com/paper/2309.15135/) (2023)
- [Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering](https://mlsanity.com/paper/2403.03448/) (2024)
- [Granular-Ball-Induced Multiple Kernel K-Means](https://mlsanity.com/paper/2506.18637/) (2025)
- [Multi-view Clustering via Unified Multi-kernel Learning and Matrix Factorization](https://mlsanity.com/paper/2412.09065/) (2024)
- [Manifold Clustering with Schatten p-norm Maximization](https://mlsanity.com/paper/2504.20390/) (2025)
- [Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery](https://mlsanity.com/paper/2507.12029/) (2025)

## Abstract (arXiv)

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.