Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise.
Albert AkhrievJakub MarecekAndrea SimonettoPublished in: CoRR (2018)
Keyphrases
- low rank
- low rank matrix
- rank minimization
- measurement noise
- low rank subspace
- matrix completion
- low rank representation
- singular value decomposition
- low rank matrices
- matrix decomposition
- linear combination
- robust principal component analysis
- missing data
- matrix factorization
- convex optimization
- low rank approximation
- sparsity constraints
- singular values
- nuclear norm
- sparse matrix
- data matrix
- high order
- kernel matrix
- machine learning
- affinity matrix
- approximation methods
- semi supervised
- binary matrices
- high dimensional data
- small number
- feature extraction
- low dimensional
- computer vision
- low rank and sparse
- high dimensional
- norm minimization
- sparse coding
- data sets