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Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data
, Article Pattern Recognition ; Volume 43, Issue 8 , August , 2010 , Pages 2982-2992 ; 00313203 (ISSN) ; Bagheri Shouraki, S ; Sharif University of Technology
2010
Abstract
The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric...