SimpleMKKM: Simple Multiple Kernel K-means
This work addresses clustering challenges in machine learning, offering an incremental improvement for multi-kernel methods.
The paper tackles the problem of multi-kernel clustering by proposing SimpleMKKM, which extends supervised kernel alignment to clustering and shows improved performance, outperforming state-of-the-art alternatives on 11 benchmark datasets.
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.