图 书 馆
通过块正则化回归选择特征Feature Selection via Block-Regularized Regression |
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| 课程网址: | http://videolectures.net/cmulls08_kim_fsbrr/ |
| 主讲教师: | Seyoung Kim |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-10-21 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one typically encounters in problems such as whole genome association (WGA) mapping, remains an open problem in statistical learning. We propose a block-regularized regression model for sparse variable selection in a high-dimensional space where the covariates are linearly ordered, and are possibly subject to local statistical linkages (e.g., block structures) due to spacial or temporal proximity of the features. Our goal is to identify a small subset of relevant covariates that are not merely from random positions in the ordering, but grouped as contiguous blocks from large number of ordered covariates. Following a typical linear regression framework between the features and the response, our proposed model employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a 1st-order Markovian process along the feature sequence that "activates" the regression coefficients in a coupled fashion. We describe a sampling-based learning algorithm and demonstrate the performance of our method on simulated and biological data for marker identification under WGA. |
| 关 键 词: | 特征空间; 块正则化回归模型; 全基因组关联 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 74 |