Year: 2011
Type: article
Abstract: Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, spar-sity of the solution and speed of the opti-mization process. In this paper we look at the MKL problem at the same time from a learning and optimization point of view. So, instead of designing a regularizer and then struggling to find an efficient method to minimize it, we design the regularizer while keeping the optimization algorithm in mind. Hence, we introduce a novel MKL formula-tion, which mixes elements of p-norm and elastic-net kind of regularization. We also propose a fast stochastic gradient descent method that solves the novel MKL formula-tion. We show theoretically and empirically that our method has 1) state-of-the-art per-formance on many classification tasks; 2) ex-act sparse solutions with a tunable level of sparsity; 3) a convergence rate bound that depends only logarithmically on the num-ber of kernels used, and is independent of the sparsity required; 4) independence on the particular convex loss function used. 1.
Authors: Francesco Orabona, Jie Luo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2128035438