图 书 馆
正则稀疏核慢特征分析Regularized Sparse Kernel Slow Feature Analysis |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_boehmer_regularized/ |
| 主讲教师: | Wendelin Böhmer |
| 开课单位: | 第六电气工程学院 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent variables from time series. Generative relationships are usually complex, and current algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to provide a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small but complex data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. Versatility and performance of our method are demonstrated on audio and video data sets. |
| 关 键 词: | 核慢特征分析（SFA）算法; 时间序列特征; 稀疏核技巧 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-24：yumf |
| 阅读次数: | 121 |