Spectral Partitioning of Large and Sparse Tensors using Low-Rank Tensor Approximation.
Lars EldénMaryam DehghanPublished in: CoRR (2020)
Keyphrases
- low rank
- high order
- tensor factorization
- tensor decomposition
- frobenius norm
- low rank approximation
- matrix factorization
- low rank matrix
- low rank subspace
- low rank matrices
- rank minimization
- nuclear norm
- matrix decomposition
- higher order
- low rank representation
- matrix completion
- robust principal component analysis
- singular value decomposition
- trace norm
- kernel matrix
- missing data
- eigendecomposition
- regularized regression
- diffusion tensor
- collaborative filtering
- convex optimization
- singular values
- linear combination
- semi supervised
- pairwise
- normalized cut
- nonnegative matrix factorization
- markov random field
- data matrix
- high dimensional data
- graph partitioning
- negative matrix factorization
- tensor field
- sparse coding
- sparse approximation
- natural images
- data sets
- recommender systems
- approximation methods
- image processing