人类活动的高效深度聚类及其改进评价方法

Louis Mahon, Thomas Lukasiewicz
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引用次数: 0

摘要

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。
Efficient Deep Clustering of Human Activities and How to Improve Evaluation
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.
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