Sparse Supernodal Solver Using Block Low-Rank Compression.
Gregoire PichonEric DarveMathieu FavergePierre RametJean RomanPublished in: IPDPS Workshops (2017)
Keyphrases
- low rank
- low rank matrix
- rank minimization
- low rank matrices
- low rank subspace
- nuclear norm
- low rank representation
- sparsity constraints
- robust principal component analysis
- group sparsity
- kernel matrices
- convex optimization
- matrix factorization
- linear combination
- missing data
- singular value decomposition
- regularized regression
- matrix decomposition
- matrix completion
- low rank approximation
- tensor decomposition
- semi supervised
- high order
- high dimensional data
- trace norm
- kernel matrix
- minimization problems
- sparse matrix
- singular values
- image compression
- sparse coding
- affinity matrix
- high dimensional
- data sets
- low rank and sparse
- data matrix
- signal recovery
- compressive sensing
- sparse representation
- nearest neighbor
- active learning
- optical flow
- machine learning