Unsupervised Deep Metric Learning via Orthogonality Based Probabilistic Loss
Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar · IEEE Transactions on Artificial Intelligence · 2020
Metric learning is an important problem in machine learning. It aims to group similar observations together. Existing state-of-the-art metric learning approaches require class labels to induce a metric. Sometimes it is expensive or not possible to collect these labels. In this paper, we propose an unsupervised learning approach that learns a metric without making use of class labels. The lack of class labels is compensated by obtaining pseudo-labels of data using a graph-based clustering approach. The pseudo-labels are used to form triplets of examples, which guide the metric learning process. We propose a probabilistic loss function that minimizes the chances of each triplet violating an angular constraint. A weight function and an orthogonality constraint in the objective speed up convergence and avoid a model collapse. We also provide a stochastic formulation of our method to scale up to large-scale datasets. Our studies demonstrate the competitiveness of our approach against state-of-the-art methods.