Safe Triplet Screening For Distance Metric Learning

Authors:
Tomoki Yoshida Nagoya Institute of Technology
Ichiro Takeuchi Nagoya Institute of Technology, National Institute for Material Science, RIKEN Center for Advanced Intelligence Project
Masayuki Karasuyama Nagoya Institute of Technology, National Institute for Material Science, Japan Science and Technology Agency

Introduction:

The authors study safe screening for metric learning.

Abstract:

We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet screening identifies triplets which can be safely removed from the optimization problem without losing the optimality. Compared with existing safe screening studies, triplet screening is particularly significant because of (1) the huge number of possible triplets, and (2) the semi-definite constraint in the optimization. We derive several variants of screening rules, and analyze their relationships. Numerical experiments on benchmark datasets demonstrate the effectiveness of safe triplet screening.

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