Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees.
Roland SchurigAndreas HimmelRolf FindeisenPublished in: ECC (2024)
Keyphrases
- data driven
- dimensionality reduction
- principal component analysis
- low dimensional
- high dimensionality
- high dimensional data
- high dimensional
- feature extraction
- data representation
- structure preserving
- geometric information
- euclidean distance
- random projections
- principal components
- dimensionality reduction methods
- feature selection
- manifold learning
- geometric structure
- linear discriminant analysis
- lower dimensional
- kernel pca
- feature space
- pattern recognition and machine learning
- input space
- closed loop
- data points
- locally linear embedding
- pattern recognition
- metric learning
- nonlinear dimensionality reduction
- linear dimensionality reduction