A joint autoencoder for prediction and its application in GPS trajectory data.
Year: 2019
Type: preprint
Abstract: In scientific fields, data sparsity greatly affects prediction performance. This study builds a deep learning-based scheme called a joint autoencoder (JAE), which utilizes auxiliary information to mitigate data sparsity. The proposed scheme achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. Experiments are implemented on a GPS trajectory dataset, and the results demonstrate that the JAE is more accurate and robust than some state-of-the-art methods.
Source: arXiv (Cornell University)
Authors: Baogui Xin, Wei Peng
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2938439366