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通过最大熵谱降维，包括讨论劳伦斯范德华maatenSpectral Dimensionality Reduction via Maximum Entropy, incl. discussion by Laurens van der Maaten |
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| 课程网址: | http://videolectures.net/aistats2011_lawrence_spectral/ |
| 主讲教师: | Neil D. Lawrence, Laurens van der Maaten |
| 开课单位: | 代尔夫特工业大学 |
| 开课时间: | 2011-05-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting probabilistic models are based on GRFs. The resulting model is a nonlinear generalization of principal component analysis. We show that parameter fitting in the locally linear embedding is approximate maximum likelihood in these models. We develop new algorithms that directly maximize the likelihood and show that these new algorithms are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the GRF via the graphical lasso. |
| 关 键 词: | 谱降维; 高斯随机域; 概率模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：zyk |
| 阅读次数: | 48 |