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An RKHS for Multi-View Learning and Manifold Co-Regularization

An RKHS for Multi-View Learning and Manifold Co-Regularization

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. 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 co-regularization. In... Show More


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