A compression-based dissimilarity measure for multi-task clustering

Nguyen Huy Thach, Hao Shao, Bin Tong, Einoshin Suzuki

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

5 Citations (Scopus)

Abstract

Virtually all existing multi-task learning methods for string data require either domain specific knowledge to extract feature representations or a careful setting of many input parameters. In this work, we propose a feature-free and parameter-light multi-task clustering algorithm for string data. To transfer knowledge between different domains, a novel dictionary-based compression dissimilarity measure is proposed. Experimental results with extensive comparisons demonstrate the generality and the effectiveness of our proposal.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Proceedings
Pages123-132
Number of pages10
DOIs
Publication statusPublished - Jul 14 2011
Event19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011 - Warsaw, Poland
Duration: Jun 28 2011Jun 30 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6804 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011
CountryPoland
CityWarsaw
Period6/28/116/30/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Thach, N. H., Shao, H., Tong, B., & Suzuki, E. (2011). A compression-based dissimilarity measure for multi-task clustering. In Foundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Proceedings (pp. 123-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6804 LNAI). https://doi.org/10.1007/978-3-642-21916-0_14