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KDD
2010
ACM

Semi-supervised sparse metric learning using alternating linearization optimization

13 years 5 months ago
Semi-supervised sparse metric learning using alternating linearization optimization
In plenty of scenarios, data can be represented as vectors 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 ...
Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu,
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where KDD
Authors Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, Peng Liu
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