Universal learning over related distributions and adaptive graph transduction

Erheng Zhong, Wei Fan, Jing Peng, Olivier Verscheure, Jiangtao Ren

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

2 Scopus citations

Abstract

The basis assumption that "training and test data drawn from the same distribution" is often violated in reality. In this paper, we propose one common solution to cover various scenarios of learning under "different but related distributions" in a single framework. Explicit examples include (a) sample selection bias between training and testing data, (b) transfer learning or no labeled data in target domain, and (c) noisy or uncertain training data. The main motivation is that one could ideally solve as many problems as possible with a single approach. The proposed solution extends graph transduction using the maximum margin principle over unlabeled data. The error of the proposed method is bounded under reasonable assumptions even when the training and testing distributions are different. Experiment results demonstrate that the proposed method improves the traditional graph transduction by as much as 15% in accuracy and AUC in all common situations of distribution difference. Most importantly, it outperforms, by up to 10% in accuracy, several state-of-art approaches proposed to solve specific category of distribution difference, i.e, BRSD [1] for sample selection bias, CDSC [2] for transfer learning, etc. The main claim is that the adaptive graph transduction is a general and competitive method to solve distribution differences implicitly without knowing and worrying about the exact type. These at least include sample selection bias, transfer learning, uncertainty mining, as well as those alike that are still not studied yet. The source code and datasets are available from the authors.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings
Pages678-693
Number of pages16
EditionPART 2
DOIs
Publication statusPublished - 19 Oct 2009
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009 - Bled, Slovenia
Duration: 7 Sep 200911 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5782 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009
CountrySlovenia
CityBled
Period7/09/0911/09/09

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Zhong, E., Fan, W., Peng, J., Verscheure, O., & Ren, J. (2009). Universal learning over related distributions and adaptive graph transduction. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings (PART 2 ed., pp. 678-693). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5782 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-04174-7_44