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 ...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, Peng Liu
• Keywords: Data Mining, KDD 2010, Metric Learning, Metric Learning Methods, Sparse Metric Learning