Multiview point cloud kernels for semisupervised learning [Lecture Notes]
Rosenberg, David, Sindhwani, Vikas, Bartlett, Peter L., & Niyogi, Partha (2009) Multiview point cloud kernels for semisupervised learning [Lecture Notes]. IEEE Signal Processing Magazine, 26(5), 145-150 .
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.
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|Item Type:||Journal Article|
|Keywords:||Approximation error, Semisupervised learning, Kernel, Signal processing algorithms, Support vector machines , Hilbert space, Estimation error, Clouds, Convergence|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2009 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||17 Aug 2011 22:49|
|Last Modified:||01 Mar 2012 02:11|
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