图 书 馆
基于监督矩阵复杂度的主动特征获取Active Feature Acquisition with supervised Matrix Completition |
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| 课程网址: | http://videolectures.net/kdd2018_huang_acquisition_matrix/ |
| 主讲教师: | Sheng-Jun Huang |
| 开课单位: | 南京航空航天大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature acquisition usually involves special devices or complex processes, it is expensive to acquire all feature values for the whole dataset. On the other hand, features may be correlated with each other, and some values may be recovered from the others. It is thus important to decide which features are most informative for recovering the other features as well as improving the learning performance. In this paper, we try to train an effective classification model with the least acquisition cost by jointly performing active feature querying and supervised matrix completion. When completing the feature matrix, a novel objective function is proposed to simultaneously minimize the reconstruction error on observed entries and the supervised loss on training data. When querying the feature value, the most uncertain entry is actively selected based on the variance of previous iterations. In addition, a bi-objective optimization method is presented for cost-aware active selection when features bear different acquisition costs. The effectiveness of the proposed approach is well validated by both theoretical analysis and experimental study. |
| 关 键 词: | 基于监督矩阵复杂度; 主动特征获取; 双目标优化方法; 主动特征查询和监督矩阵 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-16：cyh |
| 最后编审: | 2023-03-16：cyh |
| 阅读次数: | 28 |