图 书 馆
多子空间表示和发现Multi-Subspace Representation and Discovery |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_ding_subspace/ |
| 主讲教师: | Chris Ding |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper presents the multi-subspace discovery problem and provides a theoretical solution which is guaranteed to recover the number of subspaces, the dimensions of each subspace, and the members of data points of each subspace simultaneously. We further propose a data representation model to handle noisy real world data. We develop a novel optimization approach to learn the presented model which is guaranteed to converge to global optimizers. As applications of our models, we first apply our solutions as preprocessing in a series of machine learning problems, including clustering, classification, and semisupervised learning. We found that our method automatically obtains robust data presentation which preserves the affine subspace structures of high dimensional data and generate more accurate results in the learning tasks. We also establish a robust standalone classifier which directly utilizes our sparse and low rank representation model. Experimental results indicate our methods improve the quality of data by preprocessing and the standalone classifier outperforms some state-of-the-art learning approaches. |
| 关 键 词: | 多子空间; 数据点; 全局优化器 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-31：yumf |
| 阅读次数: | 75 |