Nonconvex Low-Rank Symmetric Tensor Completion from Noisy Data.
Changxiao CaiGen LiH. Vincent PoorYuxin ChenPublished in: CoRR (2019)
Keyphrases
- noisy data
- low rank
- missing data
- convex optimization
- trace norm
- low rank matrices
- frobenius norm
- robust principal component analysis
- tensor decomposition
- high order
- low rank matrix recovery
- low rank matrix
- low rank and sparse
- matrix completion
- matrix factorization
- missing values
- singular value decomposition
- rank minimization
- kernel matrix
- linear combination
- high dimensional data
- higher order
- semi supervised
- nuclear norm
- diffusion tensor
- minimization problems
- optical flow
- singular values
- convex relaxation
- pattern recognition
- training data
- active learning
- low rank approximation
- high dimensional
- principal component analysis
- dimensionality reduction
- semidefinite programming
- feature selection
- total variation
- high dimensionality