图 书 馆
广义主成分分析（GPCA）Generalized Principal Component Analysis (GPCA) |
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| 课程网址: | http://videolectures.net/mlss05au_vidal_gpca/ |
| 主讲教师: | Rene Vidal |
| 开课单位: | 约翰霍普金斯大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Data segmentation is usually though of as a chicken-and-egg problem. In order to estimate a mixture of models one needs to first segment the data, and in order to segment the data one needs to know the model parameters. Therefore, data segmentation is usually solved in two stages 1. Data clustering and 2. Model fitting. Other iterative methods use, e.g. the Expectation Maximization (EM) algorithm. This talk will show that for a wide class of segmentation problems with multi-linear structure (including clustering subspaces of unknown and varying dimensions), the chicken-and-egg dilemma can be tackled as follows: 1. Fit a set of polynomials to all data points, without clustering the data 2. Obtain the model parameters for each group from the derivatives of these polynomials. Applications of GPCA to image/video/motion segmentation, face clustering, and identification of hybrid dynamical models systems will also be presented. |
| 关 键 词: | 数据分割; 估计模型; 多线性结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-10：lxf |
| 阅读次数: | 157 |