Constraint Selection in Metric Learning
This work addresses scalability and accuracy issues in metric learning for large-scale or overlapping class data, representing an incremental improvement over existing methods.
The paper tackles the challenge of improving accuracy and scalability in iterative metric learning algorithms by introducing a loss-dependent weighted selection of constraints, which leads to better results than state-of-the-art methods in terms of both accuracy and time complexity.
A number of machine learning algorithms are using a metric, or a distance, in order to compare individuals. The Euclidean distance is usually employed, but it may be more efficient to learn a parametric distance such as Mahalanobis metric. Learning such a metric is a hot topic since more than ten years now, and a number of methods have been proposed to efficiently learn it. However, the nature of the problem makes it quite difficult for large scale data, as well as data for which classes overlap. This paper presents a simple way of improving accuracy and scalability of any iterative metric learning algorithm, where constraints are obtained prior to the algorithm. The proposed approach relies on a loss-dependent weighted selection of constraints that are used for learning the metric. Using the corresponding dedicated loss function, the method clearly allows to obtain better results than state-of-the-art methods, both in terms of accuracy and time complexity. Some experimental results on real world, and potentially large, datasets are demonstrating the effectiveness of our proposition.