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高斯过程潜变量的概率非线性主成分分析Probabilistic Non-Linear Principal Component Analysis with Gaussian Process Latent Variables |
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| 课程网址: | http://videolectures.net/mlws04_neil_pnlpc/ |
| 主讲教师: | Neil D. Lawrence |
| 开课单位: | 谢菲尔德大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It is known that Principal Component Analysis has an underlying probabilistic representation based on a latent variable model. Principal component analysis (PCA) is recovered when the latent variables are integrated out and the parameters of the model are optimised by maximum likelihood. It is less well known that the dual approach of integrating out the parameters and optimising with respect to the latent variables also leads to PCA. The marginalised likelihood in this case takes the form of Gaussian process mappings, with linear Covariance functions, from a latent space to an observed space, which we refer to as a Gaussian Process Latent Variable Model (GPLVM). This dual probabilistic PCA is still a linear latent variable model, but by looking beyond the inner product kernel as a for a covariance function we can develop a non-linear probabilistic PCA. |
| 关 键 词: | 高斯过程; 线性协方差函数; 高斯过程潜变量模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-21：王淑红(课程编辑志愿者) |
| 阅读次数: | 82 |