Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Autor: | Huo, Zepeng, PakBin, Arash, Chen, Xiaohan, Hurley, Nathan, Yuan, Ye, Qian, Xiaoning, Wang, Zhangyang, Huang, Shuai, Mortazavi, Bobak |
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Rok vydání: | 2020 |
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Druh dokumentu: | Working Paper |
Popis: | Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm. We develop a context-aware mixture of deep models termed the {\alpha}-\b{eta} network coupled with uncertainty quantification (UQ) based upon maximum entropy to enhance human activity recognition performance. We improve accuracy and F score by 10% by identifying high-level contexts in a data-driven way to guide model development. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets, demonstrating improved accuracy through unknown context discovery. Comment: 10 pages, 5 figures, accepted by AISTATS 2020 |
Databáze: | arXiv |
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