Semi-supervised projection clustering with transferred centroid regularization

Bin Tong, Hao Shao, Bin Hui Chou, Einoshin Suzuki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
ページ306-321
ページ数16
PART 3
DOI
出版ステータス出版済み - 2010
イベントEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, スペイン
継続期間: 9 20 20109 24 2010

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 3
6323 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Countryスペイン
CityBarcelona
Period9/20/109/24/10

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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