Metric learning using labeled and unlabeled data for semi-supervised/domain adaptation classification

Metric learning includes a wide range of algorithms aiming to improve the classification accuracy by capturing the spatial structure of the training set. The performance of those (supervised) methods greatly depends on the amount of labeled data available for training. In practice, however, it is usually not easy to obtain a large-scale labeled set, as opposed to an unlabeled one. In this paper we propose a new method for metric learning using a small-scale labeled set and a large-scale unlabeled set. This method can be applied in two setups - a Semi-Supervised (SS) classification setup and a Domain Adaptation (DA) setup. We used two sources of hand-written digits images to demonstrate the performance of our proposed method. We show that in both SS and DA setups, the proposed method leads to fewer classification errors compared to Euclidean distance and to Large Margin Nearest Neighbor (LMNN).