Polynomial summaries of positive semidefinite kernels

• Authors: Kilho Shin;Tetsuji Kuboyama
• Venue: Theoretical Computer Science
• Year: 2009

Polynomials have proven to be useful tools to tailor generic kernels to specific applications. Nevertheless, we had only restricted knowledge for selecting fertile polynomials which consistently produce positive semidefinite kernels. For example, the well-known polynomial kernel can only take advantage of a very narrow range of polynomials, that is, the univariate polynomials with positive coefficients. This restriction not only hinders intensive exploitation of the flexibility of the kernel method, but also causes misuse of indefinite kernels. Our main theorem significantly relaxes the restriction by asserting that a polynomial consistently produces positive semidefinite kernels, if it has a positive semidefinite coefficient matrix. This sufficient condition is quite natural, and hence, it can be a good characterization of the fertile polynomials. In fact, we prove that the converse of the assertion of the theorem also holds true in the case of degree 1. We also prove the effectiveness of our main theorem by showing three corollaries relating to certain applications known in the literature: the first and second corollaries, respectively, give generalizations of the polynomial kernel and the principal-angle (determinant) kernel. The third corollary shows extended and corrected sufficient conditions for the codon-improved kernel and the weighted-degree kernel with shifts to be positive semidefinite.