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Semi-supervised Transfer Metric Learning with Relative Constraints

  • Rakesh Kumar Sanodiya
  • Sriparna Saha
  • Jimson Mathew
  • Prateek Bangwal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)

Abstract

Distance metric learning is one of the most important aspects behind the performance of numerous algorithms under the data mining paradigm. In this article, we propose a new method for transfer metric learning under semi-supervised setting, using the concept of relative distance constraints to exploit more information from the unlabeled data present in the target task. We need an appropriate distance function for extracting useful information from unlabeled data. For this purpose, we use the concept of pairwise relative distance constraints. With the help of few labeled data, we obtain the pair-wise similarities in the form of inequality and equality constraints. We use the concept of Bregman projection to satisfy such constraints to the initial distance matrix that is composed of both labeled and unlabeled data, and then construct the appropriate K-nearest neighbor graph using this matrix, which provides better results regardless of the dimension of the data.

Keywords

Metric learning Transfer learning Semi-supervised learning Relative distance comparisons 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rakesh Kumar Sanodiya
    • 1
  • Sriparna Saha
    • 1
  • Jimson Mathew
    • 1
  • Prateek Bangwal
    • 2
  1. 1.Indian Institute of Technology PatnaPatnaIndia
  2. 2.University of Petroleum and Energy StudiesDehradunIndia

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