Subspace Aware Recovery of Low Rank and Jointly Sparse Signals.
Sampurna BiswasSoura DasguptaRaghuraman MudumbaiMathews JacobPublished in: IEEE Trans. Computational Imaging (2017)
Keyphrases
- low rank
- low rank representation
- rank minimization
- regularized regression
- low rank matrix
- convex optimization
- missing data
- linear combination
- high dimensional data
- low rank subspace
- matrix factorization
- low rank matrices
- singular value decomposition
- nuclear norm
- matrix completion
- eigendecomposition
- matrix decomposition
- high order
- robust principal component analysis
- kernel matrix
- signal processing
- minimization problems
- kernel matrices
- semi supervised
- data matrix
- group sparsity
- affinity matrix
- dimensionality reduction
- pattern recognition
- subspace learning
- sparse matrix
- singular values
- image processing
- tensor decomposition
- trace norm
- machine learning
- sparse coding
- principal component analysis
- least squares
- nearest neighbor
- high dimensional
- pairwise