An efficient multiple kernel learning in reproducing kernel Hilbert spaces (RKHS)

Abstract

The reproducing kernel Hilbert space construction is a bijection or transform theory which associates a positive definite kernel with a Hilbert space of functions. Recently, reproducing kernel Hilbert space (RKHS) has come wildly alive in the pattern recognition and machine learning community. In this paper, we propose a novel method named multiple kernel learning with reproducing property (MKLRP) to achieve some classification tasks. The MKLRP consists of two major steps. First, we find the basic solution of a generalized differential operator by delta function, and prove this basic solution is a new specific reproducing kernel called H2-reproducing kernel (HRK) in RKHS. Second, in RKHS, we prove that the HRK satisfies the condition of Mercer kernel. Furthermore, a novel specific multiple kernel learning (MKL) called MKLRP, which is based on reproducing kernel is proposed. We perform an extensive experimental evaluation on synthetic and real-world data, which shows the effectiveness of the proposed approach.