图 书 馆
基于交替线性化优化的半监督稀疏度量学习Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization |
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| 课程网址: | http://videolectures.net/kdd2010_liu_sss/ |
| 主讲教师: | Wei Liu |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 欧几里得空间; 机器学习; 数据挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-11：lxf |
| 阅读次数: | 31 |