Implementation of a Native Regularization Scheme Named As Fantope for Similarity Search

Authors(1) :-Divya Mishra

This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning. To this end, we propose to incorporate in the objective function a linear regularization term that minimizes the k smallest eigenvalues of the distance matrix. It is equivalent to minimizing the trace of the product of the distance matrix with a matrix in the convex hull of rank-k projection matrices, called a Fantope. Based on this new regularization method, we derive an optimization scheme to efficiently learn the distance matrix. We demonstrate the effectiveness of the method on synthetic and challenging real datasets of face verification and image classification with relative attributes, on which our method outperforms state-of-the-art metric learning algorithms.

Authors and Affiliations

Divya Mishra
Computer Science, Naraina Vidyapeeth Engineering and Mgmt. Institute, Kanpur, Uttar Pradesh, India

Distance Metric, Mahalanobis Distance Metric, Metric Learning Fantope Regularization, Ky Fanís Theorem

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 634-637
Manuscript Number : CSEIT172388
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Divya Mishra, "Implementation of a Native Regularization Scheme Named As Fantope for Similarity Search", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.634-637, May-June-2017.
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