Transfer spectral clustering

Wenhao Jiang, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

29 Citations (Scopus)

Abstract

Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages789-803
Number of pages15
EditionPART 2
DOIs
Publication statusPublished - 4 Oct 2012
Event2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom
Duration: 24 Sep 201228 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7524 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
Country/TerritoryUnited Kingdom
CityBristol
Period24/09/1228/09/12

Keywords

  • Co-clustering
  • Spectral Clustering
  • Transfer Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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