A Geometric Approach for Unsupervised Similarity Learning

Abstract

Metric learning groups similar examples together, while moving away dissimilar ones. This is a crucial task in image processing and computer vision. However, existing metric learning approaches require huge number of labeled examples for their success. In this paper, we propose a novel, unsupervised metric learning approach, that learns a similarity metric without making use of class labels. Using a graph-based clustering approach, we form a set of tuples, to provide constraints for metric learning. To efficiently handle high-dimensional data, we learn the metric in a lower dimensional latent space. A confidence function is devised to aid the convergence by appropriately weighting the loss functions. The parameters of our approach are jointly learned using Riemannian optimization.