Name
Abstract | ||
|---|---|---|
•A novel framework termed joint graph optimization and projection learning (JGOPL) is proposed for graph-based dimensionality reduction.•The l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved.•In order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization.•The locality constraint and graph optimization strategy proposed is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.03.024 | Pattern Recognition |
Keywords | Field | DocType |
Graph optimization,Projection learning,Dimensionality reduction,Robustness | Spectral clustering,Locality,Dimensionality reduction,Outlier,Exploit,Theoretical computer science,Robustness (computer science),Artificial intelligence,Cluster analysis,Feature learning,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
92 | 1 | 0031-3203 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yugen Yi | 1 | 92 | 15.25 |
| Jianzhong Wang | 2 | 214 | 17.72 |
| Zhou Wei | 3 | 24 | 22.00 |
| Yuming Fang | 4 | 1247 | 75.50 |
| Jun Kong | 5 | 111 | 18.94 |
| Yinghua Lu | 6 | 103 | 14.30 |