No. 962

Title:

Sparse representation and piece-wise linear kernel

Author(s):

Ikeda, Shiro (The Instutute of Statistical Mathematics)

Key words:

sparse representation; overcomplete basis; kernel

Abstract:

    We propose a new type of kernel function, where feature space is explicitly given with a piece-wise linear mapping from the input space. This idea is inspired by sparse linear system analysis, where inputs are represented as a sparse linear combination of ``dictionary vectors.'' This article gives the idea of such kernel function, and some preliminary experimental results.