Recovering low-rank and sparse matrix based on the truncated nuclear norm.
Feilong CaoJiaying ChenHailiang YeJianwei ZhaoZhenghua ZhouPublished in: Neural Networks (2017)
Keyphrases
- low rank and sparse
- low rank
- nuclear norm
- low rank matrix
- convex optimization
- matrix completion
- matrix factorization
- singular value decomposition
- missing data
- linear combination
- rank minimization
- semi supervised
- high dimensional data
- high order
- singular values
- sparse matrix
- norm minimization
- data matrix
- pattern recognition
- high dimensional
- low rank approximation
- approximation methods
- signal recovery
- feature space
- feature selection
- collaborative filtering
- minimization problems
- interior point methods
- active learning
- data sets
- higher order