自主监督的对比表示学习用于半监督的时间序列分类
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2023
概括
本研究介绍了通过时间和上下文对比 (TS-TCC) 进行时间序列表示学习,这是一种从未标记的时间序列数据中学习的新框架. 它的性能与监督方法相提并论,即使使用有限的标记数据.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 从没有标记或标记很少的时间序列数据中学习有效的表示是具有挑战性的.
- 对比的自我监督学习已经成为一种强大的技术,用于从未标记的数据中进行表示学习.
- 现有的方法可能无法完全捕捉时间序列数据中固有的时间动态和上下文信息.
研究的目的:
- 提出一个新的框架,时间序列表示学习通过时间和上下文对比 (TS-TCC),用于从未标记的数据中学习可靠的时间序列表示.
- 调查时间序列特定数据增强策略在对比学习中的影响.
- 将框架扩展到半监督设置 (Class-Aware TS-TCC) 以利用有限的标记数据.
主要方法:
- 开发了TS-TCC,包括时间对比和上下文对比模块.
- 引入了特定于时间序列的弱和强数据增强.
- 拟议的Class-Aware TS-TCC (CA-TCC) 使用伪标签用于半监督学习中的类意识对比损失.
- 进行了一项关于时间序列数据增强选择的系统研究.
主要成果:
- 通过线性评估,TS-TCC通过线性评估学习能够实现与完全监督培训相当的表现的表示.
- 拟议的框架在少量学习和转移学习场景中表现出高效率.
- 通过利用有限的标记数据,CA-TCC有效地改善了表示.
结论:
- TS-TCC提供了一种强大的方法,用于对时间序列数据进行自我监督的表示学习.
- 该框架在具有有限标记数据的场景中提供了显著的优势.
- 提出的方法推进了时间序列表示学习的最新技术.
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