Toward Grassmannian Dimensionality Reduction in MPC.
Roland SchurigAndreas HimmelRolf FindeisenPublished in: IEEE Control. Syst. Lett. (2023)
Keyphrases
- dimensionality reduction
- subspace learning
- high dimensional data
- graph embedding
- high dimensional
- low dimensional
- high dimensionality
- principal component analysis
- preprocessing step
- data representation
- pattern recognition
- data points
- linear discriminant analysis
- dimension reduction
- closed loop
- manifold learning
- input space
- dimensionality reduction methods
- pattern recognition and machine learning
- feature selection
- feature space
- dynamic model
- feature extraction
- structure preserving
- sparse representation
- linear projection
- discriminant analysis
- intrinsic dimensionality
- nonlinear dimensionality reduction
- linear dimensionality reduction
- euclidean distance
- kernel learning
- random projections
- principal components
- linear quadratic
- machine learning
- geometrical structure
- multidimensional scaling
- semi supervised
- singular value decomposition
- real time