Ratio-based multiple kernel clustering

Abstract

Maximum margin clustering (MMC) approaches extend the large margin principle of SVM to unsupervised learning with considerable success. In this work, we utilize the ratio between the margin and the intra-cluster variance, to explicitly consider both the separation and the compactness of the clusters in the objective. Moreover, we employ multiple kernel learning (MKL) to jointly learn the kernel and a partitioning of the instances, thus overcoming the kernel selection problem of MMC. Importantly, the margin alone cannot reliably reflect the quality of the learned kernel, as it can be enlarged by a simple scaling of the kernel. In contrast, our ratio-based objective is scale invariant and also invariant to the type of norm constraints on the kernel parameters. Optimization of the objective is performed using an iterative gradient-based algorithm. Comparative clustering experiments on various datasets demonstrate the effectiveness of the proposed formulation. © 2014 Springer-Verlag.