Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization
文献类型：会议论文
| 作者 | Wei Liu; Shiqian Ma; Dacheng Tao; Jianzhuang Liu; Peng Liu |
| 出版日期 | 2010 |
| 会议名称 | 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010 |
| 会议地点 | New York |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/2767]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2010 |
| 推荐引用方式 GB/T 7714 | Wei Liu,Shiqian Ma,Dacheng Tao,et al. Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization[C]. 见:16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010. New York. |
入库方式： OAI收割
来源：深圳先进技术研究院
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