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

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

研究成果: Contribution to journalArticle査読

13 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)251-276
ページ数26
ジャーナルKnowledge and Information Systems
36
1
DOI
出版ステータス出版済み - 7 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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