Name
Abstract | ||
|---|---|---|
Marginal independence constraints play an important role in learning with graphical models. One way of parameterizing a model of marginal independencies is by building a latent variable model where two independent observed variables have no common latent source. In sparse domains, however, it might be advantageous to model the marginal ob- served distribution directly, without explic- itly including latent variables in the model. There have been recent advances in Gaussian and binary models of marginal independence, but no models with non-linear dependencies between continuous variables has been pro- posed so far. In this paper, we describe how to generalize the Gaussian model of marginal independencies based on mixtures, and how to learn parameters. This requires a non- standard parameterization and raises difficult non-linear optimization issues. |
Year | Venue | Keywords |
|---|---|---|
2009 | AISTATS | latent variable,mixture of gaussians,graphical model,latent variable model |
Field | DocType | Volume |
Econometrics,Computer science,Latent class model,Latent variable,Artificial intelligence,Local independence,Marginal model,Mathematical optimization,Latent variable model,Gaussian,Graphical model,Machine learning,Mixture model | Journal | 5 |
Citations | PageRank | References |
4 | 0.62 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Bezerra de Andrade e Silva | 1 | 109 | 24.56 |
| Zoubin Ghahramani | 2 | 10455 | 1264.39 |