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Multiview Point Cloud Kernels for Semisupervised Learning
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Multiview Point Cloud Kernels for Semisupervised Learning
The Lecture Notes column of the September 2009 issue of the IEEE Signal Processing Magazine presents a framework called multiview point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces. Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL.
Read the full article by David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, and Partha Niyogi.
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