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Low-rank geometric mean metric learning

2018-06-14Code Available0· sign in to hype

Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra

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Abstract

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.

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Metric Learning

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