Name
Abstract | ||
|---|---|---|
•A novel time series feature selection task with labeled and unlabeled data.•A new semi-supervised time series feature learning model is proposed.•The model integrates least square minimization, spectral analysis, scaled pseudo labels as well as time series feature similarity regularization terms.•Experiments on real-world data demonstrating significant performance gain of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2018.12.026 | Pattern Recognition |
Keywords | Field | DocType |
Time series,Feature selection,Semi-supervised learning,Classification | Least squares,Time series,Semi-supervised learning,Feature selection,Pattern recognition,Regularization (mathematics),Artificial intelligence,Spectral analysis,Discriminative model,Feature learning,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
89 | 1 | 0031-3203 |
Citations | PageRank | References |
5 | 0.44 | 32 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai-Shuai Wang | 1 | 62 | 13.11 |
| Qin Zhang | 2 | 47 | 13.66 |
| Jia Wu | 3 | 620 | 65.55 |
| Shirui Pan | 4 | 820 | 69.37 |
| Yixin Chen | 5 | 4326 | 299.19 |