An Online Kernel Learning Algorithm based on Orthogonal Matching Pursuit

Abstract

Matching pursuit algorithms learn a function that is weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the least-squares sense. Experimental result shows that it is an effective method, but the drawbacks are that this algorithm is not appropriate to online learning or estimating the strongly nonlinear functions. In this paper, we present a kind of online kernel learning algorithm based on orthogonal matching pursuit. The orthogonal matching pursuit is employed not only to guide our online learning algorithm to estimate the target function but also to keep control of the sparsity of the solution. And the introduction of “kernel trick” can effective reduce the error when it is used to estimate the nonlinear functions. At last, a kind of nonlinear two-dimensional “sinc” function is used to test our algorithm and the results are compared with the well-known SVMTorch on Support Vectors percent and root mean square error which approve that our online learning algorithm is effective.