图 书 馆
在大规模词典上学习高维数据的稀疏表示Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries |
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| 课程网址: | http://videolectures.net/nips2011_xiang_dictionaries/ |
| 主讲教师: | Zhen James Xiang |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  在数据自适应词典上学习稀疏表示是对数据建模的最新方法。但是，当字典很大且数据量很大时，这是一个计算难题。我们探讨了问题的三个方面。首先，我们得出了新的，经过重大改进的筛选测试，可以快速确定保证零权重的码字。其次，我们在学习稀疏表示的情况下研究随机投影的属性。最后，我们开发了一个层次结构的框架，该框架使用增量随机投影和筛选在较小的阶段中学习用于稀疏表示的层次结构的字典。实验结果表明，该框架可以更有效地学习信息性的稀疏表示。 |
| 课程简介: | Learning sparse representations on data adaptive dictionaries is a state-of-the-art method for modeling data. But when the dictionary is large and the data dimension is high, it is a computationally challenging problem. We explore three aspects of the problem. First, we derive new, greatly improved screening tests that quickly identify codewords that are guaranteed to have zero weights. Second, we study the properties of random projections in the context of learning sparse representations. Finally, we develop a hierarchical framework that uses incremental random projections and screening to learn, in small stages, a hierarchically structured dictionary for sparse representations. Empirical results show that our framework can learn informative hierarchical sparse representations more efficiently. |
| 关 键 词: | 数据建模; 信息性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-12：zyk |
| 最后编审: | 2020-11-12：zyk |
| 阅读次数: | 39 |