Low-rank geometric mean metric learning

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Jun 14, 2018
Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra
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We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of the algorithm. This allows to jointly learn a low-dimensional subspace where the data reside and the Mahalanobis metric that appropriately fits the data. Our results show that we compete effectively with GMML at lower ranks.

* Accepted to the geometry in machine learning (GiMLi) workshop at ICML 2018  
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