Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10115-012-0550-5 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
Universal dissimilarity measure, Multi-task clustering, Kolmogorov complexity, Cross-task clustering | Data mining,Kolmogorov complexity,Computer science,Feature extraction,Video content analysis,Artificial intelligence,Cluster analysis,Generality,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 1 | 0219-3116 |
Citations | PageRank | References |
4 | 0.40 | 39 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thach Huy Nguyen | 1 | 17 | 2.73 |
| Hao Shao | 2 | 19 | 3.36 |
| Bin Tong | 3 | 40 | 8.11 |
| Einoshin Suzuki | 4 | 853 | 93.41 |