Skip to Main content Skip to Navigation
Conference papers

Online Semi-Supervised Learning on Quantized Graphs

Michal Valko 1 Branislav Kveton 2 Huang Ling 3 Ting Daniel 4
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Friday, November 18, 2011 - 12:25:07 AM
Last modification on : Thursday, January 20, 2022 - 4:12:32 PM
Long-term archiving on: : Sunday, February 19, 2012 - 2:21:06 AM


Files produced by the author(s)


  • HAL Id : hal-00642361, version 1



Michal Valko, Branislav Kveton, Huang Ling, Ting Daniel. Online Semi-Supervised Learning on Quantized Graphs. Uncertainty in Artificial Intelligence, Jun 2010, Catalina Island, United States. ⟨hal-00642361⟩



Les métriques sont temporairement indisponibles