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Classification with partial labels

Published: 24 August 2008 Publication History

Abstract

In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are represented as a set of possible labels for each training example, one of which is the correct label. We introduce a discriminative learning approach that incorporates partial label information into the conventional margin-based learning framework. The partial label learning problem is formulated as a convex quadratic optimization minimizing the L2-norm regularized empirical risk using hinge loss. We also present an efficient algorithm for classification in the presence of partial labels. Experiments with different data sets show that partial label information improves the performance of classification when there is traditional fully-labeled data, and also yields reasonable performance in the absence of any fully labeled data.

References

[1]
A. Asuncion and D. Newman. UCI machine learning repository. www.ics.uci.edu/~mlearn/MLRepository.html, 2007.
[2]
A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning distance functions using equivalence relations. In Proceedings of 20th International Conference on Machine Learning, 2003.
[3]
M. Bilenko, S. Basu, and R. J. Mooney. Semi-supervised clustering by seeding. In Proceedings of 19th International Conference on Machine Learning, 2002.
[4]
M. Bilenko, S. Basu, and R. J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of 21th International Conference on Machine Learning, 2004.
[5]
C.-C. Chang and C.-J. Lin. Libsvm data. www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/, 2001.
[6]
S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
[7]
D. Cohn, R. Caruana, and A. McCallum. Semi-supervised clustering with user feedback. In Cornell University Technical Report TR2003-1892, 2003.
[8]
F. Cozman, I. Cohen, and M. Cirelo. Semi-supervised learning of mixture models and bayesian networks. In Proceedings of the Twentieth International Conference of Machine Learning, 2003.
[9]
K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265--292, 2001.
[10]
J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. Information-theoretic metric learning. In Proceedings of the 24th International Conference on Machine Learning, 2007.
[11]
A. Globerson and S. Roweis. Metric learning by collapsing classes. In Advances in Neural Information Processing Systems (NIPS), 2005.
[12]
J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov. Neighbourhood components analysis. In Advances in Neural Information Processing Systems (NIPS), 2004.
[13]
D. Klein, S. D. Kamvar, and C. D. Manning. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In Proceedings of 19th International Conference on Machine Learning, 2002.
[14]
M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In Advances in Neural Information Processing Systems (NIPS), 2004.
[15]
S. Shalev-Shwartz, Y. Singer, and A. Y. Ng. Online and batch learning of pseudo-metrics. In Proceedings of the 21st International Conference on Machine Learning, 2004.
[16]
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In Proceedings of the 24th International Conference on Machine Learning, pages 807--814, New York, NY, USA, 2007. ACM.
[17]
K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with background knowledge. In Proceedings of 18th International Conference on Machine Learning, 2001.
[18]
K. Q. Weinberger, J. Blitzer, and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. In Advances ing Neural Information Processing Systems (NIPS), 2006.
[19]
E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In Advances in Neural Information Processing Systems 15, 2003.
[20]
R. Yan, J. Zhang, J. Yang, and A. G. Hauptmann. A discriminative learning framework with pairwise constraints for video object classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):578--593, 2006.
[21]
J. Zhang and R. Yan. On the value of pairwise constraints in classification and consistency. In Proceedings of the 24th International Conference on Machine Learning, pages 1111--1118, 2007.
[22]
X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005.

Cited By

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  • (2024)ULAREFProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693755(41456-41472)Online publication date: 21-Jul-2024
  • (2024)Learning with partial-label and unlabeled dataProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693347(31614-31628)Online publication date: 21-Jul-2024
  • (2024)Partial label learning with a partnerProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i13.29424(15029-15037)Online publication date: 20-Feb-2024
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Published In

cover image ACM Conferences
KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2008
1116 pages
ISBN:9781605581934
DOI:10.1145/1401890
  • General Chair:
  • Ying Li,
  • Program Chairs:
  • Bing Liu,
  • Sunita Sarawagi
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2008

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Author Tags

  1. partial labels
  2. support vectors

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2024)ULAREFProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693755(41456-41472)Online publication date: 21-Jul-2024
  • (2024)Learning with partial-label and unlabeled dataProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693347(31614-31628)Online publication date: 21-Jul-2024
  • (2024)Partial label learning with a partnerProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i13.29424(15029-15037)Online publication date: 20-Feb-2024
  • (2024)Partial label learning for automated classification of single-cell transcriptomic profilesPLOS Computational Biology10.1371/journal.pcbi.101200620:4(e1012006)Online publication date: 5-Apr-2024
  • (2024)Variational Label Enhancement for Instance-Dependent Partial Label LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.345526046:12(11298-11313)Online publication date: Dec-2024
  • (2024)PiCO+: Contrastive Label Disambiguation for Robust Partial Label LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3342650(1-15)Online publication date: 2024
  • (2024)On the Robustness of Average Losses for Partial-Label LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3275249(1-15)Online publication date: 2024
  • (2024)Partial Sequence Labeling With Structured Gaussian ProcessesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319172635:2(2783-2792)Online publication date: Feb-2024
  • (2024)Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336772136:8(3765-3782)Online publication date: Aug-2024
  • (2024)Partial Label Feature Selection: An Adaptive ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336569136:8(4178-4191)Online publication date: Aug-2024
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