Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares.
Arthur SzlamAndrew TullochMark TygertPublished in: SIAM J. Matrix Anal. Appl. (2017)
Keyphrases
- low rank approximation
- alternating least squares
- singular value decomposition
- iterative algorithms
- subspace learning
- low rank
- nonnegative matrix factorization
- kernel matrix
- spectral clustering
- data dependent
- adjacency matrix
- reconstruction error
- latent semantic indexing
- negative matrix factorization
- dimensionality reduction
- sparse representation
- feature extraction
- matrix factorization
- convex optimization
- stochastic gradient descent
- least squares
- pairwise