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Semi-supervised Learning with Regularized Laplacian
Semi-supervised Learning with Regularized Laplacian
Abstract : We study a semi-supervised learning method based on the similarity graph and Regularized
Laplacian. We give convenient optimization formulation of the Regularized Laplacian method and establish
its various properties. In particular, we show that the kernel of the method
can be interpreted in terms of discrete and continuous time random walks and possesses several important
properties of proximity measures. Both optimization and linear algebra methods can be used for efficient
computation of the classification functions. We demonstrate on numerical examples that the
Regularized Laplacian method is competitive with respect to the other state of the art semi-supervised
learning methods.
https://hal.inria.fr/hal-01184812
Contributor : Konstantin Avrachenkov <>
Submitted on : Monday, August 17, 2015 - 6:52:10 PM
Last modification on : Tuesday, June 1, 2021 - 9:10:01 AM
Long-term archiving on: : Wednesday, November 18, 2015 - 12:19:10 PM
Contributor : Konstantin Avrachenkov <>
Submitted on : Monday, August 17, 2015 - 6:52:10 PM
Last modification on : Tuesday, June 1, 2021 - 9:10:01 AM
Long-term archiving on: : Wednesday, November 18, 2015 - 12:19:10 PM
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RR-8765.pdf
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