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.
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Thach, N.H., Shao, H., Tong, B., Suzuki, E. (2011). A Compression-Based Dissimilarity Measure for Multi-task Clustering. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_14
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DOI: https://doi.org/10.1007/978-3-642-21916-0_14
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