TY - GEN
T1 - A compression-based dissimilarity measure for multi-task clustering
AU - Thach, Nguyen Huy
AU - Shao, Hao
AU - Tong, Bin
AU - Suzuki, Einoshin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79960136042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960136042&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21916-0_14
DO - 10.1007/978-3-642-21916-0_14
M3 - Conference contribution
AN - SCOPUS:79960136042
SN - 9783642219153
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 123
EP - 132
BT - Foundations of Intelligent Systems - 19th International Symposium, ISMIS 2011, Proceedings
T2 - 19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011
Y2 - 28 June 2011 through 30 June 2011
ER -