Multi-view self-supervised learning for multivariate variable-channel time series

Autor:	Brüsch, Thea, Schmidt, Mikkel N., Alstrøm, Tommy S.
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels. We propose learning one encoder to operate on all input channels individually. We then use a message passing neural network to extract a single representation across channels. We demonstrate the potential of this method by pretraining our model on a dataset with six EEG channels and then fine-tuning it on a dataset with two different EEG channels. We compare models with and without the message passing neural network across different contrastive loss functions. We show that our method, combined with the TS2Vec loss, outperforms all other methods in most settings. Comment: To appear in proceedings of 2023 IEEE International workshop on Machine Learning for Signal Processing
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.09614 Zobrazit plný text záznamu View this record from Arxiv