Efficient Deep Clustering of Human Activities and How to Improve Evaluation

Louis Mahon, Thomas Lukasiewicz
{"title":"Efficient Deep Clustering of Human Activities and How to Improve Evaluation","authors":"Louis Mahon, Thomas Lukasiewicz","doi":"10.48550/arXiv.2209.08335","DOIUrl":null,"url":null,"abstract":"There has been much recent research on human activity re\\-cog\\-ni\\-tion (HAR), due to the proliferation of wearable sensors in watches and phones, and the advances of deep learning methods, which avoid the need to manually extract features from raw sensor signals. A significant disadvantage of deep learning applied to HAR is the need for manually labelled training data, which is especially difficult to obtain for HAR datasets. Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult. In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated, describing these problems in detail and conducting careful experiments to explicate the effect that they can have on results. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models. Additionally, we present a new deep clustering model for HAR. When tested under our proposed settings, our model performs better than (or on par with) existing models, while also being more efficient and better able to scale to more complex datasets by avoiding the need for an autoencoder.","PeriodicalId":119756,"journal":{"name":"Asian Conference on Machine Learning","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Conference on Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2209.08335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

There has been much recent research on human activity re\-cog\-ni\-tion (HAR), due to the proliferation of wearable sensors in watches and phones, and the advances of deep learning methods, which avoid the need to manually extract features from raw sensor signals. A significant disadvantage of deep learning applied to HAR is the need for manually labelled training data, which is especially difficult to obtain for HAR datasets. Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult. In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated, describing these problems in detail and conducting careful experiments to explicate the effect that they can have on results. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models. Additionally, we present a new deep clustering model for HAR. When tested under our proposed settings, our model performs better than (or on par with) existing models, while also being more efficient and better able to scale to more complex datasets by avoiding the need for an autoencoder.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
人类活动的高效深度聚类及其改进评价方法
由于手表和手机中可穿戴传感器的普及,以及深度学习方法的进步,最近有很多关于人类活动感知(HAR)的研究,这些方法避免了从原始传感器信号中手动提取特征的需要。深度学习应用于HAR的一个显著缺点是需要手动标记训练数据,这对于HAR数据集来说尤其难以获得。在无监督环境中,以深度HAR聚类模型的形式开始取得进展,该模型可以在没有任何标签的情况下为数据分配标签,但是在评估深度HAR聚类模型时存在问题,这使得评估该领域和设计新方法变得困难。在本文中,我们强调了评估深度HAR聚类模型的几个不同问题,详细描述了这些问题,并进行了仔细的实验来解释它们对结果的影响。然后,我们讨论了这些问题的解决方案,并提出了未来深度HAR聚类模型的标准评估设置。此外,我们还提出了一种新的HAR深度聚类模型。当在我们提出的设置下进行测试时,我们的模型比现有模型表现得更好(或与之相当),同时也更有效,并且通过避免对自动编码器的需要,能够更好地扩展到更复杂的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
RoLNiP: Robust Learning Using Noisy Pairwise Comparisons AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network On the Interpretability of Attention Networks Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1