Greedy unsupervised multiple kernel learning

Abstract

Multiple kernel learning (MKL) has emerged as a powerful tool for considering multiple kernels when the appropriate representation of the data is unknown. Some of these kernels may be complementary, while others irrelevant to the learning task. In this work we present an MKL method for clustering. The intra-cluster variance objective is extended by learning a linear combination of kernels, together with the cluster labels, through an iterative procedure. Closed-form updates for the combination weights are derived, that greatly simplify the optimization. Moreover, to allow for robust kernel mixtures, a parameter that regulates the sparsity of the weights is incorporated into our framework. Experiments conducted on a collection of images reveal the effectiveness of the proposed method. © 2012 Springer-Verlag.