图 书 馆
正则化稀疏核慢特征分析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: | 柏林大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  本文开发了一种内核化的慢特征分析（SFA）算法。 SFA是一种无监督的学习方法，可以从时间序列中提取对潜在变量进行编码的特征。生成关系通常很复杂，并且当前的算法不够强大或趋于过度拟合。我们将内核技巧与稀疏化结合使用，为大型数据集提供了功能强大的函数类。稀疏性是通过一种新颖的匹配追踪方法实现的，该方法也可以应用于其他任务。但是，对于较小但复杂的数据集，内核SFA方法会导致过度拟合和数值不稳定。为了实施稳定的解决方案，我们在SFA目标中引入了正则化。我们的方法的多功能性和性能在音频和视频数据集上得到了证明。 |
| 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: | 慢特征分析; 数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-20：zyk |
| 最后编审: | 2021-03-20：zyk |
| 阅读次数: | 63 |