Deep learning and low rank dictionary model for mHealth data classification | IEEE Conference Publication | IEEE Xplore

Deep learning and low rank dictionary model for mHealth data classification


Abstract:

In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classif...Show More

Abstract:

In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.
Date of Conference: 25-29 June 2018
Date Added to IEEE Xplore: 30 August 2018
ISBN Information:
Electronic ISSN: 2376-6506
Conference Location: Limassol, Cyprus

References

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