publication . Preprint . 2016

Semi-supervised Kernel Metric Learning Using Relative Comparisons

Amid, Ehsan; Gionis, Aristides; Ukkonen, Antti;
Open Access English
  • Published: 30 Nov 2016
Abstract
We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unkn...
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning
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35 references, page 1 of 3

Ehsan Amid, Aristides Gionis, and Antti Ukkonen. A kernel-learning approach to semi-supervised clustering with relative distance comparisons. In ECML PKDD, 2015. [OpenAIRE]

S. Anand, S. Mittal, O. Tuzel, and P. Meer. Semi-supervised kernel mean shift clustering. PAMI, 36 (6):1201-1215, 2014.

Sugato Basu, Arindam Banerjee, and Raymond J. Mooney. Active semi-supervision for pairwise constrained clustering. In SDM, 2004a.

Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney. A probabilistic framework for semisupervised clustering. In KDD, 2004b.

Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In ICML, 2004. [OpenAIRE]

L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7(3):200 - 217, 1967. [OpenAIRE]

D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. PAMI, 24 (5):603-619, 2002.

Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S Dhillon. Information-theoretic metric learning. In ICML, 2007. [OpenAIRE]

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. A unified view of kernel k-means, spectral clustering and graph cuts. Technical Report TR-04-25, University of Texas, 2005.

Gene H. Golub and Charles F. Van Loan. Matrix Computations (3rd Ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996. ISBN 0-8018-5414-8.

Hannes Heikinheimo and Antti Ukkonen. The crowd-median algorithm. In First AAAI Conference on Human Computation and Crowdsourcing, 2013.

Eric Heim, Matthew Berger, Lee M. Seversky, and Milos Hauskrecht. Efficient online relative comparison kernel learning. arXiv:1501.01242, 2015.

Geoffrey Hinton and Sam Roweis. Stochastic neighbor embedding. In NIPS, 2003.

Lawrence Hubert and Phipps Arabie. Comparing partitions. Journal of Classification, 2(1):193-218, 1985. ISSN 0176-4268. doi: 10.1007/BF01908075. URL http://dx.doi.org/10.1007/ BF01908075. [OpenAIRE]

Prateek Jain, Brian Kulis, Jason V. Davis, and Inderjit S. Dhillon. Metric and kernel learning using a linear transformation. J. Mach. Learn. Res., 13:519-547, 2012.

35 references, page 1 of 3
Abstract
We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unkn...
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning
Download from
35 references, page 1 of 3

Ehsan Amid, Aristides Gionis, and Antti Ukkonen. A kernel-learning approach to semi-supervised clustering with relative distance comparisons. In ECML PKDD, 2015. [OpenAIRE]

S. Anand, S. Mittal, O. Tuzel, and P. Meer. Semi-supervised kernel mean shift clustering. PAMI, 36 (6):1201-1215, 2014.

Sugato Basu, Arindam Banerjee, and Raymond J. Mooney. Active semi-supervision for pairwise constrained clustering. In SDM, 2004a.

Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney. A probabilistic framework for semisupervised clustering. In KDD, 2004b.

Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In ICML, 2004. [OpenAIRE]

L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7(3):200 - 217, 1967. [OpenAIRE]

D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. PAMI, 24 (5):603-619, 2002.

Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S Dhillon. Information-theoretic metric learning. In ICML, 2007. [OpenAIRE]

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. A unified view of kernel k-means, spectral clustering and graph cuts. Technical Report TR-04-25, University of Texas, 2005.

Gene H. Golub and Charles F. Van Loan. Matrix Computations (3rd Ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996. ISBN 0-8018-5414-8.

Hannes Heikinheimo and Antti Ukkonen. The crowd-median algorithm. In First AAAI Conference on Human Computation and Crowdsourcing, 2013.

Eric Heim, Matthew Berger, Lee M. Seversky, and Milos Hauskrecht. Efficient online relative comparison kernel learning. arXiv:1501.01242, 2015.

Geoffrey Hinton and Sam Roweis. Stochastic neighbor embedding. In NIPS, 2003.

Lawrence Hubert and Phipps Arabie. Comparing partitions. Journal of Classification, 2(1):193-218, 1985. ISSN 0176-4268. doi: 10.1007/BF01908075. URL http://dx.doi.org/10.1007/ BF01908075. [OpenAIRE]

Prateek Jain, Brian Kulis, Jason V. Davis, and Inderjit S. Dhillon. Metric and kernel learning using a linear transformation. J. Mach. Learn. Res., 13:519-547, 2012.

35 references, page 1 of 3
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