Abstract

In this paper, we introduce two novel metric learning algorithms, -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: -LMNN inherits the computational benefits of a linear mapping from linear metric learning, but uses a non-linear -distance to explicitly capture similarities within histogram data sets; GB-LMNN applies gradient-boosting to learn non-linear mappings directly in function space and takes advantage of this approach’s robustness, speed, parallelizability and insensitivity towards the single additional hyperparameter. 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 -LMNN, obtain best results in 19 out of 20 learning settings.