Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome.
Damian BrzyskiXixi HuJoaquín GoñiBeau M. AncesTimothy W. RandolphJaroslaw HarezlakPublished in: IEEE Trans. Biomed. Eng. (2024)
Keyphrases
- low rank
- regularized regression
- sparse regression
- low rank matrix
- rank minimization
- linear combination
- convex optimization
- singular value decomposition
- matrix factorization
- missing data
- matrix decomposition
- low rank subspace
- semi supervised
- matrix completion
- nuclear norm
- low rank matrices
- low rank representation
- robust principal component analysis
- high dimensional data
- kernel matrices
- kernel matrix
- data matrix
- low rank approximation
- high order
- singular values
- minimization problems
- sparsity constraints
- frobenius norm
- trace norm
- importance sampling
- group lasso
- low rank and sparse
- factorization methods
- learning algorithm
- affinity matrix
- least squares
- active learning
- binary matrix
- tensor decomposition
- pattern recognition
- image processing