Abstract
We propose a novel method, called Semi-supervised Projection Clustering in Transfer Learning (SPCTL), where multiple source domains and one target domain are assumed. Traditional semi-supervised projection clustering methods hold the assumption that the data and pairwise constraints are all drawn from the same domain. However, many related data sets with different distributions are available in real applications. The traditional methods thus can not be directly extended to such a scenario. One major challenging issue is how to exploit constraint knowledge from multiple source domains and transfer it to the target domain where all the data are unlabeled. To handle this difficulty, we are motivated to construct a common subspace where the difference in distributions among domains can be reduced. We also invent a transferred centroid regularization, which acts as a bridge to transfer the constraint knowledge to the target domain, to formulate this geometric structure formed by the centroids from different domains. Extensive experiments on both synthetic and practical data sets show the effectiveness of our method.
| Original language | English |
|---|---|
| Title of host publication | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings |
| Pages | 306-321 |
| Number of pages | 16 |
| Edition | PART 3 |
| DOIs | |
| Publication status | Published - Oct 25 2010 |
| Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spain Duration: Sep 20 2010 → Sep 24 2010 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 3 |
| Volume | 6323 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 9/20/10 → 9/24/10 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Semi-supervised projection clustering with transferred centroid regularization. / Tong, Bin; Shao, Hao; Chou, Bin Hui; Suzuki, Einoshin.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings. PART 3. ed. 2010. p. 306-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6323 LNAI, No. PART 3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Semi-supervised projection clustering with transferred centroid regularization
AU - Tong, Bin
AU - Shao, Hao
AU - Chou, Bin Hui
AU - Suzuki, Einoshin
PY - 2010/10/25
Y1 - 2010/10/25
N2 - We propose a novel method, called Semi-supervised Projection Clustering in Transfer Learning (SPCTL), where multiple source domains and one target domain are assumed. Traditional semi-supervised projection clustering methods hold the assumption that the data and pairwise constraints are all drawn from the same domain. However, many related data sets with different distributions are available in real applications. The traditional methods thus can not be directly extended to such a scenario. One major challenging issue is how to exploit constraint knowledge from multiple source domains and transfer it to the target domain where all the data are unlabeled. To handle this difficulty, we are motivated to construct a common subspace where the difference in distributions among domains can be reduced. We also invent a transferred centroid regularization, which acts as a bridge to transfer the constraint knowledge to the target domain, to formulate this geometric structure formed by the centroids from different domains. Extensive experiments on both synthetic and practical data sets show the effectiveness of our method.
AB - We propose a novel method, called Semi-supervised Projection Clustering in Transfer Learning (SPCTL), where multiple source domains and one target domain are assumed. Traditional semi-supervised projection clustering methods hold the assumption that the data and pairwise constraints are all drawn from the same domain. However, many related data sets with different distributions are available in real applications. The traditional methods thus can not be directly extended to such a scenario. One major challenging issue is how to exploit constraint knowledge from multiple source domains and transfer it to the target domain where all the data are unlabeled. To handle this difficulty, we are motivated to construct a common subspace where the difference in distributions among domains can be reduced. We also invent a transferred centroid regularization, which acts as a bridge to transfer the constraint knowledge to the target domain, to formulate this geometric structure formed by the centroids from different domains. Extensive experiments on both synthetic and practical data sets show the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=77958058027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77958058027&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15939-8_20
DO - 10.1007/978-3-642-15939-8_20
M3 - Conference contribution
AN - SCOPUS:77958058027
SN - 3642159389
SN - 9783642159381
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 306
EP - 321
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
ER -