Semi-supervised projection clustering with transferred centroid regularization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
Pages306-321
Number of pages16
EditionPART 3
DOIs
Publication statusPublished - Oct 25 2010
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spain
Duration: Sep 20 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6323 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
CountrySpain
CityBarcelona
Period9/20/109/24/10

Fingerprint

Centroid
Regularization
Clustering
Projection
Experiments
Target
Transfer Learning
Geometric Structure
Projection Method
Clustering Methods
Pairwise
Subspace
Scenarios
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tong, B., Shao, H., Chou, B. H., & Suzuki, E. (2010). Semi-supervised projection clustering with transferred centroid regularization. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings (PART 3 ed., pp. 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). https://doi.org/10.1007/978-3-642-15939-8_20

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 proceedingConference contribution

Tong, B, Shao, H, Chou, BH & Suzuki, E 2010, Semi-supervised projection clustering with transferred centroid regularization. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6323 LNAI, pp. 306-321, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010, Barcelona, Spain, 9/20/10. https://doi.org/10.1007/978-3-642-15939-8_20
Tong B, Shao H, Chou BH, Suzuki E. Semi-supervised projection clustering with transferred centroid regularization. In 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); PART 3). https://doi.org/10.1007/978-3-642-15939-8_20
Tong, Bin ; Shao, Hao ; Chou, Bin Hui ; Suzuki, Einoshin. / Semi-supervised projection clustering with transferred centroid regularization. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings. PART 3. ed. 2010. pp. 306-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{19e9ff8df7d248a4a183fa7aafc4b5ae,
title = "Semi-supervised projection clustering with transferred centroid regularization",
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.",
author = "Bin Tong and Hao Shao and Chou, {Bin Hui} and Einoshin Suzuki",
year = "2010",
month = "10",
day = "25",
doi = "10.1007/978-3-642-15939-8_20",
language = "English",
isbn = "3642159389",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "306--321",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings",
edition = "PART 3",

}

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 -