Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

• URL: http://arxiv.org/abs/2203.12230v1
• Date: Wed, 23 Mar 2022 06:54:16 GMT
• Title: Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
• Authors: Jinqiang Wang, Tao Zhu, Liming Chen, Huansheng Ning, Yaping Wan
• Abstract summary: We propose a new contrastive learning framework that negative selection by clustering in Human Activity Recognition (HAR) Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric.
• Score: 5.351176836203563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.

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