图 书 馆
通过光谱正则化选择不同的特征Selecting Diverse Features via Spectral Regularization |
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| 课程网址: | http://videolectures.net/machine_das_spectral/ |
| 主讲教师: | Abhimanyu Das |
| 开课单位: | 微软公司 |
| 开课时间: | 2013-06-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of diverse feature selection in linear regression: selecting a small subset of diverse features that can predict a given objective. Diversity is useful for several reasons such as interpretability, robustness to noise, etc. We propose several spectral regularizers that capture a notion of diversity of features and show that these are all submodular set functions. These regularizers, when added to the objective function for linear regression, result in approximately submodular functions, which can then be maximized approximately by efficient greedy and local search algorithms, with provable guarantees. We compare our algorithms to traditional greedy and ℓ1-regularization schemes and show that we obtain a more diverse set of features that result in the regression problem being stable under perturbations. |
| 关 键 词: | 线性回归; 特征选择; 可解释性 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-04-23：chenxin |
| 阅读次数: | 48 |