Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kerne...

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Bibliographic Details
Published in:IEEE signal processing magazine 2009-09, Vol.26 (5), p.145-150
Main Authors: Rosenberg, D., Sindhwani, V., Bartlett, P., Niyogi, P.
Format: Magazinearticle
Language:English
Subjects:
Approximation error
Clouds
Convergence
Estimation error
Hilbert space
Kernel
Semisupervised learning
Signal processing algorithms
Support vector machines
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Summary:In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), 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), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.
ISSN:1053-5888
1558-0792
DOI:10.1109/MSP.2009.933383