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