图 书 馆
不完整数据的功能集嵌入Feature Set Embedding for Incomplete Data |
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| 课程网址: | http://videolectures.net/nips2010_grangier_fse/ |
| 主讲教师: | David Grangier |
| 开课单位: | 美国国家实验室 |
| 开课时间: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a new learning strategy for classification problems in which train and/or test data suffer from missing features. In previous work, instances are represented as vectors from some feature space and one is forced to impute missing values or to consider an instance-specific subspace. In contrast, our method considers instances as sets of (feature,value) pairs which naturally handle the missing value case. Building onto this framework, we propose a classification strategy for sets. Our proposal maps (feature,value) pairs into an embedding space and then non-linearly combines the set of embedded vectors. The embedding and the combination parameters are learned jointly on the final classification objective. This simple strategy allows great flexibility in encoding prior knowledge about the features in the embedding step and yields advantageous results compared to alternative solutions over several datasets. |
| 关 键 词: | 测试数据; 缺失值; 组合参数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-12-15：chenxin |
| 阅读次数: | 58 |