An RKHS for multi-view learning and manifold co-regularization

Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs) such that (a) the chosen functions make similar predictions on unlabeled examples, and (b) the average prediction given by the chosen functions performs well on labeled examples. In this paper, we construct a single RKHS with a data-dependent "co-regularization" norm that reduces these approaches to standard supervised learning. The reproducing kernel for this RKHS can be explicitly derived and plugged into any kernel method, greatly extending the theoretical and algorithmic scope of coregularization. In particular, with this development, the Rademacher complexity bound for co-regularization given in (Rosenberg & Bartlett, 2007) follows easily from wellknown results. Furthermore, more refined bounds given by localized Rademacher complexity can also be easily applied. We propose a co-regularizat...

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Vikas Sindhwani, David S. Rosenberg
• Keywords: ICML 2008, Localized Rademacher Complexity, Machine Learning, Multi-view Semi-supervised Kernel, Multiple Reproducing Kernel