Multiview point cloud kernels for semisupervised learning [Lecture Notes]

Abstract

In semisupervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multiview point cloud regularization (MVPCR) [5], which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS) [7], [3], [6], manifold regularization (MR) [1], [8], [4], and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multiview kernel. © 2009 IEEE.