Nearest Hit-Misses Component Analysis for Supervised Metric Learning

Abstract

Metric learning is the task of learning a distance metric from training data that reasonably identifies the important relationships between the data. An appropriate distance metric is of considerable importance for building accurate classifiers. In this paper, we propose a novel supervised metric learning method, nearest hit-misses component analysis. In our method, the margin is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different class), and then the distance metric is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. We further introduce a regularization term to alleviate overfitting. Moreover, the proposed method can perform metric learning and dimensionality reduction simultaneously. Comparative experiments with the state-of-the-art metric learning methods on various real-world data sets demonstrate the effectiveness of the proposed method.