We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we re-formulate the problem as a smooth minimization one, which can be solved efficiently using a reduced gradient descent algorithm. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. Comprehensive experiments on 11 benchmark datasets demonstrate that SimpleMKKM outperforms state of the art multi-kernel clustering alternatives.
@article{arxiv.2005.04975,
title = {SimpleMKKM: Simple Multiple Kernel K-means},
author = {Xinwang Liu and En Zhu and Jiyuan Liu and Timothy Hospedales and Yang Wang and Meng Wang},
journal= {arXiv preprint arXiv:2005.04975},
year = {2020}
}