Multiple Kernel Learning Using Sparse Representation

Abstract

This paper introduces a kernel machine for multiclass discrimination where the scoring function for each class is constructed using a linear combination over a predefined diverse library of kernel functions. The scoring function is built using an expanded set of the kernel library hence increasing the number of degrees of freedom to analyze the information content of each data sample. To choose the smallest set of kernels that best match desirable first-order moment properties of the class-conditional distribution a regularized linear least-squares problem is solved. The proposed multi-kernel machine is then demonstrated and benchmarked against similar techniques which rely on the use of a single kernel using a satellite imagery dataset for the purposes of discriminating among several vegetation and soil types.