Georgia Institute of Technology College of Sciences

Reproducing kernel Hilbert spaces (RKHS) have recently emerged as a powerful mathematical framework for many problems in machine learning, statistics, and their applications. In this talk, we will present a formulation in vector-valued RKHS that provides a unified treatment of several important machine learning approaches. Among these, one is Manifold Regularization, which seeks to exploit the geometry of the input data via unlabeled examples, and one is Multi-view Learning, which attempts to integrate different features and modalities in the input data. Numerical results on several challenging multi-class classification problems demonstrate the competitive practical performance of our approach.