图 书 馆
能量相关成分分析的结构方程和分裂归一化Structural equations and divisive normalization for energy-dependent component analysis |
|
| 课程网址: | http://videolectures.net/nips2011_hirayama_equations/ |
| 主讲教师: | Jun-ichiro Hirayama |
| 开课单位: | 京都大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Components estimated by independent component analysis and related methods are typically not independent in real data. A very common form of nonlinear dependency between the components is correlations in their variances or energies. Here, we propose a principled probabilistic model to model the energy- correlations between the latent variables. Our two-stage model includes a linear mixing of latent signals into the observed ones like in ICA. The main new fea- ture is a model of the energy-correlations based on the structural equation model (SEM), in particular, a Linear Non-Gaussian SEM. The SEM is closely related to divisive normalization which effectively reduces energy correlation. Our new two- stage model enables estimation of both the linear mixing and the interactions related to energy-correlations, without resorting to approximations of the likelihood function or other non-principled approaches. We demonstrate the applicability of our method with synthetic dataset, natural images and brain signals. |
| 关 键 词: | 图形模型; 计算机科学; 机器学习; 主成分分析 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 57 |