Deep fusion of heterogeneous sensor data

Zuozhu Liu, Wenyu Zhang, Tony Q.S. Quek, Shaowei Lin

研究成果: Conference contribution

8 引文 斯高帕斯(Scopus)

摘要

Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. In this paper, we propose a neural network-based multimodal data fusion framework named deep multimodal encoder (DME). Through our new objective function, both the intra- and inter-modal correlations of multimodal sensor data can be better exploited for recovering the missing values, and the shared representation learned can be used directly for prediction tasks. In experiments with real-world sensor data, DME shows remarkable ability for missing data imputation and new modality prediction. Compared with traditional algorithms such as kNN and Sparse-PCA, DME is more expressive, robust, and scalable to large datasets.

原文English
主出版物標題2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5965-5969
頁數5
ISBN(電子)9781509041176
DOIs
出版狀態Published - 2017 六月 16
事件2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
持續時間: 2017 三月 52017 三月 9

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
國家/地區United States
城市New Orleans
期間17-03-0517-03-09

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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