publication . Preprint . 2014

Active Metric Learning from Relative Comparisons

Xiong, Sicheng; Rosales, Rómer; Pei, Yuanli; Fern, Xiaoli Z.;
Open Access English
  • Published: 15 Sep 2014
Abstract
This work focuses on active learning of distance metrics from relative comparison information. A relative comparison specifies, for a data point triplet $(x_i,x_j,x_k)$, that instance $x_i$ is more similar to $x_j$ than to $x_k$. Such constraints, when available, have been shown to be useful toward defining appropriate distance metrics. In real-world applications, acquiring constraints often require considerable human effort. This motivates us to study how to select and query the most useful relative comparisons to achieve effective metric learning with minimum user effort. Given an underlying class concept that is employed by the user to provide such constraint...
Subjects
free text keywords: Computer Science - Learning
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20 references, page 1 of 2

[1] S. Basu, A. Banerjee, and R. Mooney. Active semi-supervision for pairwise constrained clustering. In Proceedings of the SIAM International Conference on Data mining, pages 333-344, 2004.

[2] L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.

[3] F. Briggs, X. Z. Fern, and R. Raich. Rank-loss support instance machines for miml instance annotation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pages 534-542, New York, NY, USA, 2012. ACM.

[4] T. Cover and P. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information theory, 13(1):21-27, 1967. [OpenAIRE]

[5] T. Cover and J. Thomas. Elements of Information Theory. Wiley, 1991.

[6] A. Frank and A. Asuncion. UCI machine learning repository, 2010.

[7] D. Greene and P. Cunningham. Constraint selection by committee: An ensemble approach to identifying informative constraints for semi-supervised clustering. pages 140-151, 2007.

[8] D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of ACM SIGIR International Conference on Research and development in information retrieval, pages 3-12, 1994.

[9] M. Little, P. McSharry, S. Roberts, D. Costello, and I. Moroz. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine, 6(1):23, 2007.

[10] P. Mallapragada, R. Jin, and A. Jain. Active query selection for semi-supervised clustering. In Proceedings of International Conference on Pattern Recognition, pages 1-4, 2008. [OpenAIRE]

[11] J. Nunnally and I. Bernstein. Psychometric Theory. McGraw Hill, Inc., 1994.

[12] R. Rosales and G. Fung. Learning sparse metrics via linear programming. In Proceedings of ACM SIGKDD International Conference on Knowledge discovery and data mining, pages 367-373, 2006.

[13] N. Roy and A. Mccallum. Toward optimal active learning through sampling estimation of error reduction. In Proceedings of International Conference on Machine Learning, 2001.

[14] M. Schultz and T. Joachims. Learning a distance metric from relative comparisons. In Advances in neural information processing systems, 2003.

[15] B. Settles. Active learning literature survey. 2010.

20 references, page 1 of 2
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