Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD.
Kim BatselierWenjian YuLuca DanielNgai WongPublished in: CoRR (2017)
Keyphrases
- low rank approximation
- singular value decomposition
- low rank matrix approximation
- dimensionality reduction
- singular values
- subspace learning
- low rank
- least squares
- spectral clustering
- reconstruction error
- higher order
- data dependent
- matrix completion
- high order
- matrix decomposition
- kernel matrix
- adjacency matrix
- network structure
- iterative algorithms
- data matrix
- high dimensional data
- k means
- principal component analysis
- nonnegative matrix factorization
- data representation
- text retrieval