更可靠的邻居对比学习为基于传感器的人类活动识别中的新课程发现
Mingcong Zhang1, Tao Zhu1, Mingxing Nie1
1The School of Computer Science, University of South China, Hengyang 421001, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了人类活动识别 (HAR) 系统的新型类发现 (NCD). 一个新的框架,更可靠的邻居对比学习 (MRNCL),有效地识别了未标记的传感器数据中的新活动.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 传感器数据分析数据分析
背景情况:
- 人类活动识别 (HAR) 系统利用传感器数据进行活动分类.
- 目前的HAR系统在没有监督的情况下,在未标记的数据中发现新活动类.
- 这种限制阻碍了现实世界中的应用,因为完全监督的设置是不切实际的.
研究的目的:
- 介绍HAR的新类发现 (NCD) 问题.
- 通过使用现有标记数据,从未标记的传感器数据进行新活动的分类.
- 开发一个强大的框架,用于发现无监督活动.
主要方法:
- 提出一个端到端的框架:更可靠的邻居对比学习 (MRNCL).
- MRNCL是邻近对比学习 (NCL) 的轻量级变体.
- 在嵌入空间中包含一个有效的相似度衡,用于识别可靠的k-最近邻居.
主要成果:
- 在基于传感器的HAR中,MRNCL在NCD任务上表现优于现有的方法.
- 在三个公共数据集中,为新活动类实例展示了卓越的集群性能.
- 拟议的模型有效地利用标记数据来帮助发现新型活动.
结论:
- 对于HAR来说,MRNCL在解决新类发现问题方面取得了重大进展.
- 该框架的效率和改进的邻居识别提高了无监督学习能力.
- 这项研究为在现实世界中更适应性和实用的HAR系统铺平了道路.
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