Spectral partitioning of large and sparse 3-tensors using low-rank tensor approximation.
Lars EldénMaryam DehghanPublished in: Numer. Linear Algebra Appl. (2022)
Keyphrases
- low rank
- high order
- low rank approximation
- tensor decomposition
- frobenius norm
- low rank matrix
- tensor factorization
- matrix factorization
- low rank matrices
- low rank subspace
- rank minimization
- matrix decomposition
- trace norm
- higher order
- nuclear norm
- low rank representation
- robust principal component analysis
- matrix completion
- kernel matrix
- regularized regression
- linear combination
- convex optimization
- eigendecomposition
- sparse approximation
- missing data
- singular value decomposition
- data matrix
- approximation methods
- semi supervised
- diffusion tensor
- collaborative filtering
- normalized cut
- negative matrix factorization
- pairwise
- high dimensional
- high dimensional data
- least squares
- markov random field
- feature extraction
- compressive sensing
- graph partitioning
- pattern recognition