Sufficient dimension reduction for a novel class of zero-inflated graphical models.
Eric KoplinLiliana ForzaniDiego TomassiRuth M. PfeifferPublished in: Comput. Stat. Data Anal. (2024)
Keyphrases
- graphical models
- dimension reduction
- belief propagation
- chain graphs
- probabilistic model
- probabilistic graphical models
- probabilistic inference
- principal component analysis
- feature extraction
- high dimensional
- manifold learning
- random variables
- bayesian networks
- approximate inference
- variable selection
- linear discriminant analysis
- singular value decomposition
- low dimensional
- conditional independence
- high dimensional data
- cluster analysis
- markov networks
- dimensionality reduction
- map inference
- factor graphs
- feature selection
- conditional random fields
- unsupervised learning
- high dimensionality
- expectation maximization
- image processing
- data sets
- generative model
- input data
- nearest neighbor
- feature space
- preprocessing