An integrated autoencoder-based filter for sparse big data

Abstract

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilises auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.

KEYWORDS:

• Sparse big data
• integrated autoencoder (IAE)
• data sparsity
• prediction
• filter

Acknowledgements

The authors would like to express sincere gratitude to the referees for their valuable suggestions and comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Social Science Foundation of China [No. 16FJY008]; the National Planning Office of Philosophy and Social Science [No. 11801060]; and the Natural Science Foundation of Shandong Province [No. ZR2016FM26].

Notes on contributors

Wei Peng

Wei Peng is a graduate student at Shandong University of Science and Technology, Qingdao, China. Her research interests include complex evolution systems, optimal decision and deep learning. Wei Peng has published 5 scientific papers on these topics in the most prestigious journals of Management Science and Engineering.

Baogui Xin

Baogui Xin is a full Professor at Shandong University of Science and Technology, Qingdao, China. He is on the editorial boards of Plos One, Frontiers in Physics, Frontiers in Applied Mathematics and Statistics. His research interests include complex evolution systems, artificial intelligence, fractional order nonlinear systems, AI-based optimal decision.

BX: conceptualisation. BX and WP: methodology. BX and WP: software. BX and WP: validation. BX: formal analysis. WP: writing-original draft preparation. BX and WP: writing-review and editing. BX and WP: visualization. BX: supervision, project administration, and funding acquisition.