Name
Abstract | ||
|---|---|---|
In this paper, we propose a joint conditional graphical Lasso to learn multiple conditional Gaussian graphical models, also known as Gaussian conditional random fields, with some similar structures. Our model builds on the maximum likelihood method with the convex sparse group Lasso penalty. Moreover, our model is able to model multiple multivariate linear regressions with unknown noise covariance... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2710090 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Graphical models,Random variables,Maximum likelihood estimation,Numerical models,Data models,Covariance matrices | Conditional random field,Data modeling,Random variable,Computer science,Lasso (statistics),Gaussian,Artificial intelligence,Graphical model,Machine learning,Covariance,Linear regression | Journal |
Volume | Issue | ISSN |
29 | 7 | 2162-237X |
Citations | PageRank | References |
2 | 0.40 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feihu Huang | 1 | 10 | 8.31 |
| Songcan Chen | 2 | 4148 | 191.89 |
| Sheng-Jun Huang | 3 | 475 | 27.21 |