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Efficient kernel learning from constraints and unlabeled data

Soleymani Baghshah, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICPR.2010.821
  3. Publisher: 2010
  4. Abstract:
  5. Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semi-supervised learning tasks. In the last few years, several methods have been proposed for metric learning when must-link and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis metrics, some recently introduced methods have tried to learn more flexible distance metrics using a kernel-based approach. In this paper, we consider the problem of kernel learning from both pairwise constraints and unlabeled data. We propose a method that adapts a flexible distance metric via learning a nonparametric kernel matrix. We formulate our method as an optimization problem that can be solved efficiently. Experimental evaluations show the effectiveness of our method compared to some recently introduced methods on a variety of data sets
  6. Keywords:
  7. Semi-supervised ; Efficient method ; Kernel learning ; Metric ; Optimization problem ; Pairwise constraints ; Semi-supervised ; Pattern recognition ; Supervised learning ; Optimization
  8. Source: Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 3364-3367 ; 10514651 (ISSN) ; 9780769541099 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5597507