Kai Liu and Lodewijk Brand have recently had their paper, “Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L2,1-Norm Distances”, accepted into the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).

This work studies a distance metric learning method by utilizing side information from must-link and cannot-link datasets. Instead of traditional squared l2 norm distances, an efficient iterative algorithm is proposed to solve the general min-max objectives that use l21-norm, while yielding a strictly orthogonal projection matrix. Extensive experiments are conducted, where the proposed distance metric learning method outperforms related state-of-the art methods in a variety of experimental settings.

This paper will be presented this August in Macao, China. IJCAI is a top conference in Artificial Intelligence.