Sharper Bounds for Regression and Low-Rank Approximation with Regularization.
Haim AvronKenneth L. ClarksonDavid P. WoodruffPublished in: CoRR (2016)
Keyphrases
- low rank approximation
- reproducing kernel hilbert space
- kernel matrix
- singular value decomposition
- low rank matrix approximation
- low rank
- data dependent
- subspace learning
- regression model
- lower bound
- spectral clustering
- adjacency matrix
- iterative algorithms
- latent semantic indexing
- nonnegative matrix factorization
- reconstruction error
- kernel methods
- eigendecomposition
- model selection
- linear combination
- support vector machine
- worst case
- matrix factorization
- least squares
- dimensionality reduction
- kernel function
- active learning
- support vector