图 书 馆
对称正定矩阵的广义字典学习及其在最近邻检索中的应用Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_sra_dictionary/ |
| 主讲教师: | Suvrit Sra |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce Generalized Dictionary Learning (GDL), a simple but practical framework for learning dictionaries over the manifold of positive definite matrices. We illustrate GDL by applying it to Nearest Neighbor (NN) retrieval, a task of fundamental importance in disciplines such as machine learning and computer vision. GDL distinguishes itself from traditional dictionary learning approaches by explicitly taking into account the manifold structure of the data. In particular, GDL allows performing "sparse coding" of positive definite matrices, which enables better NN retrieval. Experiments on several covariance matrix datasets show that GDL achieves performance rivaling state-of-the-art techniques. |
| 关 键 词: | 广义字典学习; 正定矩阵; 机器学习; 计算机视觉 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-30：lxf |
| 阅读次数: | 51 |