file.pdf (164.19 kB)
0/0

Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning

Download (164.19 kB)
journal contribution
posted on 01.11.1995 by Xiaojin Zhu, Jan Kandola, Zoubin Ghahramani, John D. Lafferty
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 labeled and unlabeled data in a systematic fashion. Unlike previous work using diffusion kernels and Gaussian random field kernels, a nonparametric 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 computationally feasible for large datasets. We evaluate the kernels on real datasets using support vector machines, with encouraging results.

History

Date

01/11/1995

Exports

Exports