Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning

Abstract

We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of graph Laplacians, and combine la- beled and unlabeled data in a systematic fashion. Unlike previous work using diffusion kernels and Gaussian random field kernels, a nonpara- metric kernel approach is presented that incorporates order constraints during optimization. This results in flexible kernels and avoids the need to choose among different parametric forms. Our approach relies on a quadratically constrained quadratic program (QCQP), and is compu- tationally feasible for large datasets. We evaluate the kernels on real datasets using support vector machines, with encouraging results.