Fast, Deterministic and Sparse Dimensionality Reduction.
Daniel DadushCristóbal GuzmánNeil OlverPublished in: SODA (2018)
Keyphrases
- dimensionality reduction
- high dimensional
- random projections
- sparse representation
- low dimensional
- high dimensional data
- high dimensionality
- sparse data
- dimensionality reduction methods
- compressive sensing
- data representation
- compressed sensing
- subspace learning
- principal component analysis
- feature selection
- dimension reduction
- singular value decomposition
- pattern recognition and machine learning
- lower dimensional
- data points
- linear discriminant analysis
- black box
- underlying manifold
- principal components
- nonlinear dimensionality reduction
- structure preserving
- feature space
- pattern recognition
- feature extraction
- kernel learning
- diffusion maps
- canonical correlation analysis
- input space
- principal components analysis
- computer vision
- manifold learning
- sparse coding
- regularized least squares
- sparse matrix
- regression model
- linear dimensionality reduction
- image representation
- feature set
- finite state automaton