图 书 馆
编码相似度学习距离函数Learning Distance Function by Coding Similarity |
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| 课程网址: | http://videolectures.net/icml07_kliper_ldfc/ |
| 主讲教师: | Rioe Kliper |
| 开课单位: | 耶路撒冷希伯来大学 |
| 开课时间: | 2007-06-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. "similar" point pairs. We define the similarity in information theoretic terms, as the gain in coding length when shifting from independent encoding of the pair to joint encoding. Under simple Gaussian assumptions, this formulation leads to a non-Mahalanobis similarity function which is effcient and simple to learn. This function can be viewed as a likelihood ratio test, and we show that the optimal similaritypreserving pro jection of the data is a variant of Fisher Linear Discriminant. We also show that under some naturally occurring sampling conditions of equivalence constraints, this function converges to a known Mahalanobis distance (RCA). The suggested similarity function exhibits superior performance over alternative Mahalanobis distances learnt from the same data. Its superiority is demonstrated in the context of image retrieval and graph based clustering, using a large number of data sets. |
| 关 键 词: | 相似性函数; 信息理论术语; 等价约束 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 48 |