Semi-supervised sparse metric learning using alternating linearization optimization

Authors:
Wei Liu;Shiqian Ma;Dacheng Tao;Jianzhuang Liu;Peng Liu
Affiliations:
Columbia University, New York, NY, USA;Columbia University, New York, NY, USA;Nanyang Technological University, Singapore, Singapore;The Chinese University of Hong Kong, Hong Kong, Hong Kong;Barclays Capital, New York, NY, USA
Venue:
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2010

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Abstract

In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining applications need proximity measures over data, a simple and universal distance metric is desirable, and metric learning methods have been explored to produce sensible distance measures consistent with data relationship. However, most existing methods suffer from limited labeled data and expensive training. In this paper, we address these two issues through employing abundant unlabeled data and pursuing sparsity of metrics, resulting in a novel metric learning approach called semi-supervised sparse metric learning. Two important contributions of our approach are: 1) it propagates scarce prior affinities between data to the global scope and incorporates the full affinities into the metric learning; and 2) it uses an efficient alternating linearization method to directly optimize the sparse metric. Compared with conventional methods, ours can effectively take advantage of semi-supervision and automatically discover the sparse metric structure underlying input data patterns. We demonstrate the efficacy of the proposed approach with extensive experiments carried out on six datasets, obtaining clear performance gains over the state-of-the-arts.