Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing.
Yi LiDavid P. WoodruffTaisuke YasudaPublished in: COLT (2021)
Keyphrases
- dimensionality reduction
- low dimensional
- high dimensional data
- high dimensional
- principal component analysis
- subspace learning
- manifold learning
- lower dimensional
- linear projection
- random projections
- feature extraction
- input space
- principal components
- high dimensionality
- feature space
- pattern recognition
- nonlinear dimensionality reduction
- dimension reduction
- subspace clustering
- low dimensional spaces
- singular value decomposition
- linear discriminant analysis
- data points
- data representation
- linear dimensionality reduction
- feature selection
- semi supervised dimensionality reduction
- locality preserving projections
- kernel trick
- latent space
- dimensionality reduction methods
- kernel pca
- metric learning
- euclidean space
- euclidean distance
- test cases
- bayesian networks
- image processing
- principal components analysis
- software testing
- infinite dimensional
- vector space
- feature vectors
- pairwise
- pattern recognition and machine learning