Low-Rank Tensor Completion Using Matrix Factorization Based on Tensor Train Rank and Total Variation.
Meng DingTing-Zhu HuangTeng-Yu JiXi-Le ZhaoJing-Hua YangPublished in: J. Sci. Comput. (2019)
Keyphrases
- low rank
- matrix factorization
- minimization problems
- trace norm
- total variation
- convex optimization
- tensor factorization
- high order
- collaborative filtering
- image restoration
- missing data
- matrix completion
- low rank matrix
- nuclear norm
- factorization methods
- recommender systems
- image denoising
- denoising
- higher order
- nonnegative matrix factorization
- rank minimization
- singular values
- negative matrix factorization
- kernel matrix
- linear combination
- regularization term
- singular value decomposition
- data matrix
- semi supervised
- computer vision
- stochastic gradient descent
- missing values
- data sets
- image processing
- active learning
- small number
- diffusion tensor
- data representation
- dimensionality reduction