Abstract

Metric learning algorithms produce a linear transformation of data which is optimized for a prediction task, such as nearest-neighbor classification or ranking. However, when the input data contains a large portion of noninformative features, existing methods fail to identify the relevant features, and performance degrades accordingly. In this paper, we present an efficient and robust structural metric learning algorithm which enforces group sparsity on the learned transformation, while optimizing for structured ranking output prediction. Experiments on synthetic and real datasets demonstrate that the proposed method outperforms previous methods in both highand low-noise settings.