图 书 馆
基于信息理论学习的鲁棒特征提取Robust Feature Extraction via Information Theoretic Learning |
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| 课程网址: | http://videolectures.net/icml09_yuan_rfevitl/ |
| 主讲教师: | Xiao-Tong Yuan |
| 开课单位: | 中国科学院 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 汉简 |
| 中文简介: |  |
| 课程简介: | In this paper, we present a robust feature extraction framework based on information-theoretic learning. Its formulated objective aims at dual targets, motivated by the Renyi’s quadratic entropy of the features and the Renyi’s cross entropy between features and class labels, respectively. This objective function reaps the advantages in robustness from both redescending M-estimator and manifold regularization, and can be efficiently optimized via half-quadratic optimization in an iterative manner. In addition, the popular algorithms LPP, SRDA and LapRLS for feature extraction are all justified to be the special cases within this framework. Extensive comparison experiments on several real-world data sets, with contaminated features or labels, well validate the encouraging gain in algorithmic robustness from this proposed framework. |
| 关 键 词: | 信息理论学习; 二次熵特征; 目标函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：lxf |
| 阅读次数: | 105 |