Non-linear Metric Learning

Abstract

In this paper, we introduce two novel metric learning algorithms, χ2-LMNN and GB-LMNN, which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve this goal in fundamentally different ways: χ2-LMNN inherits the computational benefits of a linear mapping from linear metric learn-ing, but uses a non-linear χ2-distance to explicitly capture similarities within his-togram data sets; GB-LMNN applies gradient-boosting to learn non-linear map-pings directly in function space and takes advantage of this approach’s robust-ness, speed, parallelizability and insensitivity towards the single additional hyper-parameter. On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of χ2-LMNN, obtain best results in 19 out of 20 learning settings. 1