Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1904.06513 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baogui Xin | 1 | 10 | 3.72 |
| Wei Peng | 2 | 138 | 24.48 |