A compression-based dissimilarity measure for multi-task clustering

Authors:
Nguyen Huy Thach;Hao Shao;Bin Tong;Einoshin Suzuki
Affiliations:
Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan;Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan;Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan;Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Venue:
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Year:
2011

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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.