A Novel Multiple Kernel Learning Method Based on the Kullback–Leibler Divergence

In this paper we develop a novel multiple kernel learning (MKL) model that is based on the idea of the multiplicative perturbation of data in a new feature space in the framework of uncertain convex programs (UCPs). In the proposed model, we utilize the Kullback–Leibler divergence to measure the difference between the estimated kernel weights and ideal kernel weights. Instead of directly handling the proposed model in the primal, we obtain the optimistic counterpart of its Langrage dual in terms of the theory of UCPs and solve it by using the alternating optimization. In the case of a varying parameter, the proposed model gives the solution path from a robust combined kernel to some combined kernel corresponding to the initially ideal kernel weights. In addition, we also give a simple strategy to select the initial kernel weights as the ideal kernel weights if any prior knowledge of kernel weights is not available. Experimental results on several data sets show that the proposed model can obtain competitive performance with some of the state-of-the-art MKL algorithms.