图 书 馆
多任务套索的分块协调下降法及其在神经语义基发现中的应用Blockwise Coordinate Descent Procedures for the Multi-Task Lasso with Applications to Neural Semantic Basis Discovery |
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| 课程网址: | http://videolectures.net/icml09_palatucci_bcdp/ |
| 主讲教师: | Mark Palatucci |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows that a closed-form Winsorization operator can be obtained for the sup-norm penalized least squares regression. This allows the algorithm to find solutions to very large-scale problems far more efficiently than existing methods. This result complements the pioneering work of Friedman, et al. (2007) for the single-task Lasso. As a case study, we use the multi-task Lasso as a variable selector to discover a semantic basis for predicting human neural activation. The learned solution outperforms the standard basis for this task on the majority of test participants, while requiring far fewer assumptions about cognitive neuroscience. We demonstrate how this learned basis can yield insights into how the brain represents the meanings of words. |
| 关 键 词: | 下降算法; 最小二乘回归; 标准基础 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 22 |