图 书 馆
正则化线性模型的部分监督特征选择Partially Supervised Feature Selection with Regularized Linear Models |
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| 课程网址: | http://videolectures.net/icml09_helleputte_psfs/ |
| 主讲教师: | Thibault Helleputt |
| 开课单位: | 卢旺大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be available in this context to bias the selection towards some dimensions (genes) a priori assumed to be more relevant. We propose a feature selection method making use of this partial supervision. It extends previous works on embedded feature selection with linear models including regularization to enforce sparsity. A practical approximation of this technique reduces to standard SVM learning with iterative rescaling of the inputs. The scaling factors depend here on the prior knowledge but the final selection may depart from it. Practical results on several microarray data sets show the benefits of the proposed approach in terms of the stability of the selected gene lists with improved classification performances. |
| 关 键 词: | 高维数据; 特征选择; 选择方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-14：chenjy |
| 最后编审: | 2023-03-14：chenjy |
| 阅读次数: | 24 |