Dimensionality reduction for regularization of sparse data-driven RANS simulations.
Pasha PiroozmandOliver BrennerPatrick JennyPublished in: J. Comput. Phys. (2023)
Keyphrases
- data driven
- dimensionality reduction
- high dimensional
- sparse representation
- random projections
- sparsity regularization
- mixed norm
- low dimensional
- elastic net
- high dimensionality
- rank minimization
- structured sparsity
- sparse data
- principal component analysis
- pattern recognition and machine learning
- high dimensional data
- pattern recognition
- kernel learning
- manifold learning
- structure preserving
- underlying manifold
- feature space
- feature extraction
- sparse approximation
- linear discriminant analysis
- data representation
- sparse coding
- data points
- kernel pca
- input space
- metric learning
- principal components
- regularization parameter
- feature selection
- image processing
- dimension reduction
- data sets
- dimensionality reduction methods
- compressive sensing
- dictionary learning
- regularization method
- regularization methods
- nonlinear dimensionality reduction
- low rank
- kernel matrices
- image classification
- nearest neighbor
- computer vision
- machine learning