SimpleMKKM: Simple Multiple Kernel K-means

• URL: http://arxiv.org/abs/2005.04975v2
• Date: Tue, 12 May 2020 14:05:04 GMT
• Title: SimpleMKKM: Simple Multiple Kernel K-means
• Authors: Xinwang Liu, En Zhu, Jiyuan Liu, Timothy Hospedales, Yang Wang, Meng Wang
• Abstract summary: We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM) Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error.
• Score: 49.500663154085586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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