Efficient Algorithm for Distance Metric Learning

Distance metric learning provides an approach to transfer knowledge from sparse labeled data to unlabeled data. The learned metric is more proper to measure the similarity of semantics among instances. The main idea of the algorithm is to create an objective function using the equivalence constraints and in-equivalence constraints and pose the problem as an optimization problem. In this paper, we proposed to unify different metric learning algorithms into semidefinite programming (SDP) framework. The classical semidefinite programming algorithms are extremely expensive on larger problem. So we discuss efficient algorithms for large-scale metric learning. We investigated a recent proposed algorithm arise from Frank-Wolfe algorithm and proposed novel strategies for acceleration based on the special structure of the problem. We compared different algorithms on 3 UCI dataset in clustering problem.