Local dimension reduction of summary statistics for likelihood-free inference.
Jukka SirénSamuel KaskiPublished in: Stat. Comput. (2020)
Keyphrases
- dimension reduction
- summary statistics
- principal component analysis
- feature extraction
- high dimensional
- high dimensional problems
- random projections
- manifold learning
- low dimensional
- high dimensional data
- discriminative information
- variable selection
- high dimensionality
- singular value decomposition
- unsupervised learning
- feature selection
- data sets
- cluster analysis
- feature space
- range queries
- linear discriminant analysis
- preprocessing
- dimensionality reduction
- pattern recognition
- partial least squares
- maximum likelihood
- data model
- dimension reduction methods
- sparse metric learning
- bayesian networks
- machine learning
- real world
- databases