图 书 馆
数据光谱学：使用卷积算子的特征空间学习混合模型Data Spectroscopy: Learning Mixture Models using Eigenspaces of Convolution Operators |
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| 课程网址: | http://videolectures.net/icml08_belkin_dslmm/ |
| 主讲教师: | Mikhail Belkin |
| 开课单位: | 俄亥俄州立大学 |
| 开课时间: | 2008-08-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we develop a spectral framework for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often used for the identification of substances through their spectrum. Treating a kernel function K(x,y) as "light" and the sampled data as "substance", the spectrum of their interaction (eigenvalues and eigenvectors of the kernel matrix K) unveils certain aspects of the underlying parametric distribution p, such as the parameters of a Gaussian mixture. Our approach extends the intuitions and analyses underlying the existing spectral techniques, such as spectral clustering and Kernel Principal Components Analysis (KPCA). We construct algorithms to estimate parameters of Gaussian mixture models, including the number of mixture components, their means and covariance matrices, which are important in many practical applications. We provide a theoretical framework and show encouraging experimental results. |
| 关 键 词: | 估算混合分布; 光谱框架; 高斯混合模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 73 |