Fair Dimensionality Reduction and Iterative Rounding for SDPs.
Jamie MorgensternSamira SamadiMohit SinghUthaipon Tao TantipongpipatSantosh S. VempalaPublished in: CoRR (2019)
Keyphrases
- dimensionality reduction
- high dimensional
- low dimensional
- linear programming
- data representation
- pattern recognition and machine learning
- high dimensional data
- feature extraction
- structure preserving
- linear dimensionality reduction
- high dimensionality
- pattern recognition
- feature selection
- principal components
- nonlinear dimensionality reduction
- input space
- approximation algorithms
- random projections
- iterative methods
- lower dimensional
- dimensionality reduction methods
- dimension reduction
- linear discriminant analysis
- principal component analysis
- feature space
- intrinsic dimensionality
- supervised dimensionality reduction
- database
- kernel pca
- kernel learning
- mixed integer
- kernel methods
- kernel function
- least squares
- data points
- pairwise
- neural network