Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization.
Long HeHo-Yin MakPublished in: CoRR (2023)
Keyphrases
- stochastic optimization
- dimensionality reduction
- principal component analysis
- principal components
- multistage
- principal components analysis
- low dimensional
- high dimensional data
- feature extraction
- high dimensional
- linear discriminant analysis
- linear dimensionality reduction
- dimensionality reduction methods
- kernel pca
- random projections
- feature space
- pattern recognition
- lower dimensional
- high dimensionality
- feature selection
- manifold learning
- dimension reduction
- input space
- reduced dimensionality
- metric learning
- data points
- singular value decomposition
- covariance matrix
- linear projection
- face recognition
- face images
- robust optimization
- kernel function
- locally linear embedding
- dynamic programming