Sublinear Cost Low Rank Approximation via Subspace Sampling.
Victor Y. PanQi LuanJohn SvadlenkaLiang ZhaoPublished in: MACIS (2019)
Keyphrases
- low rank approximation
- subspace learning
- singular value decomposition
- low rank matrix approximation
- dimensionality reduction
- low rank
- eigendecomposition
- principal component analysis
- kernel matrix
- sparse representation
- spectral clustering
- data representation
- low dimensional
- manifold learning
- feature space
- iterative algorithms
- face recognition
- nonnegative matrix factorization
- data dependent
- matrix factorization
- minimum cost
- sample size
- least squares
- high dimensional
- feature extraction
- high dimensional data
- objective function
- machine learning
- data sets