Semi-supervised learning with regularized Laplacian. (4th March 2017)

• Record Type: Journal Article
• Authors: Avrachenkov, K.; Chebotarev, P.; Mishenin, A.
• Is Part Of: Optimization methods and software. Volume 32:Number 2(2017)
• Page Start: 222
• Page End: 236
• Publication Date: 2017-03-04
• Subjects: semi-supervised learning -- graph-based learning -- regularized Laplacian -- proximity measure -- Wikipedia article classification; 68T01 -- 68T05 -- 15A16 -- 15A45 -- 97R40 -- 05C50; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2016.1193176
• Languages: English
• ISSNs: 1055-6788

Abstract

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 robust with respect to the choice of the regularization parameter and outperforms the Laplacian-based heat kernel methods.