图 书 馆
线性判别降维Linear Discriminant Dimensionality Reduction |
|
| 课程网址: | http://videolectures.net/ecmlpkdd2011_han_dimensionality/ |
| 主讲教师: | Jiawei Han |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Fisher criterion has achieved great success in dimensionality reduction. Two representative methods based on Fisher criterion are Fisher Score and Linear Discriminant Analysis (LDA). The former is developed for feature selection while the latter is designed for subspace learning. In the past decade, these two approaches are often studied independently. In this paper, based on the observation that Fisher score and LDA are complementary, we propose to integrate Fisher score and LDA in a unified framework, namely Linear Discriminant Dimensionality Reduction (LDDR). We aim at finding a subset of features, based on which the learnt linear transformation via LDA maximizes the Fisher criterion. LDDR inherits the advantages of Fisher score and LDA and is able to do feature selection and subspace learning simultaneously. Both Fisher score and LDA can be seen as the special cases of the proposed method. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a L2,1-norm constrained least square problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark face recognition data sets illustrate that the proposed method outperforms the state of the art methods arguably. |
| 关 键 词: | 计算机科学; 机器学习; 线性模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-14：yumf |
| 阅读次数: | 98 |