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比较实例的快速判别成分分析Fast Discriminative Component Analysis for Comparing Examples |
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| 课程网址: | http://videolectures.net/lce06_peltonen_fdcac/ |
| 主讲教师: | Jaakko Peltonen |
| 开课单位: | 阿尔托大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Two recent methods, Neighborhood Components Analysis (NCA) and Informative Discriminant Analysis (IDA), search for a class-discriminative subspace or discriminative components of data, equivalent to learning of distance metrics invariant to changes perpendicular to the subspace. Constraining metrics to a subspace is useful for regularizing the metrics, and for dimensionality reduction. We introduce a variant of NCA and IDA that reduces their computational complexity from quadratic to linear in the number of data samples, by replacing their purely non-parametric class density estimates with semiparametric mixtures of Gaussians. In terms of accuracy, the method is shown to perform as well as NCA on benchmark data sets, outperforming several popular linear dimensionality reduction methods. |
| 关 键 词: | 邻域成分分析; 信息判别分析; 高斯混合 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-12：lxf |
| 阅读次数: | 83 |