Graph-based Semi-supervised Learning: Realizing pointwise smoothness probabilistically

Yuan Fang, Kevin Chen Chuan Chang, Hady W. Lauw

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiom- Atizes a set of probability constraints, which ul-timately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show the advantages of PGP.

Original languageEnglish (US)
Title of host publication31st International Conference on Machine Learning, ICML 2014
PublisherInternational Machine Learning Society (IMLS)
Pages1736-1754
Number of pages19
ISBN (Electronic)9781634393973
StatePublished - Jan 1 2014
Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
Duration: Jun 21 2014Jun 26 2014

Publication series

Name31st International Conference on Machine Learning, ICML 2014
Volume2

Other

Other31st International Conference on Machine Learning, ICML 2014
CountryChina
CityBeijing
Period6/21/146/26/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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  • Cite this

    Fang, Y., Chang, K. C. C., & Lauw, H. W. (2014). Graph-based Semi-supervised Learning: Realizing pointwise smoothness probabilistically. In 31st International Conference on Machine Learning, ICML 2014 (pp. 1736-1754). (31st International Conference on Machine Learning, ICML 2014; Vol. 2). International Machine Learning Society (IMLS).