Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization

Publisher:
Association for Computing Machinery, Inc. (ACM)
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data, 2010, pp. 1139 - 1147
Issue Date:
2010-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001821OK.pdf1.28 MB
Adobe PDF
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.
Please use this identifier to cite or link to this item: