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- Title
A feature-free and parameter-light multi-task clustering framework.
- Authors
Huy, Thach; Shao, Hao; Tong, Bin; Suzuki, Einoshin
- Abstract
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.
- Subjects
FEATURE extraction; PARAMETER estimation; COMPUTER multitasking; CLUSTER analysis (Statistics); MACHINE learning; TEXT mining; DIGITAL image processing
- Publication
Knowledge & Information Systems, 2013, Vol 36, Issue 1, p251
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-012-0550-5