A feature-free and parameter-light multi-task clustering framework

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

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)


The two last decades have witnessed extensive research on multi-task learning algorithms in diverse domains such as bioinformatics, text mining, natural language processing as well as image and video content analysis. However, all existing multi-task learning methods require either domain-specific knowledge to extract features or a careful setting of many input parameters. There are many disadvantages associated with prior knowledge requirements for feature extraction or parameter-laden approaches. One of the most obvious problems is that we may find a wrong or non-existent pattern because of poorly extracted features or incorrectly set parameters. In this work, we propose a feature-free and parameter-light multi-task clustering framework to overcome these disadvantages. Our proposal is motivated by the recent successes of Kolmogorov-based methods on various applications. However, such methods are only defined for single-task problems because they lack a mechanism to share knowledge between different tasks. To address this problem, we create a novel dictionary-based compression dissimilarity measure that allows us to share knowledge across different tasks effectively. Experimental results with extensive comparisons demonstrate the generality and the effectiveness of our proposal.

ジャーナルKnowledge and Information Systems
出版ステータス出版済み - 7月 2013

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • 人工知能


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