NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning


Eli Meirom, Pavel Kisilev ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:610-619, 2016.


Multiple Kernel Learning (MKL) methods are known for their effectiveness in solving classification and regression problems involving multi-modal data. Many MKL approaches use linear combination of base kernels, resulting in somewhat limited feature representations. Several non-linear MKL formulations were proposed recently. They provide much higher dimensional feature spaces, and, therefore, richer representations. However, these methods often lead to non-convex optimization and to intractable number of optimization parameters. In this paper, we propose a new non-linear MKL method that utilizes nuclear norm regularization and leads to convex optimization problem. The proposed Nuclear-norm-Constrained MKL (NuC-MKL) algorithm converges faster, and requires smaller number of calls to an SVM solver, as compared to other competing methods. Moreover, the number of the model support vectors in our approach is usually much smaller, as compared to other methods. This suggests that our algorithm is more resilient to overfitting. We test our algorithm on several known benchmarks, and show that it equals or outperforms the state-of-the-art MKL methods on all these data sets.

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