A Sparsity Inducing Nuclear-Norm Estimator (SpINNEr) for Matrix-Variate Regression in Brain Connectivity Analysis.
Damian BrzyskiXixi HuJoaquín GoñiBeau M. AncesTimothy W. RandolphJaroslaw HarezlakPublished in: CoRR (2020)
Keyphrases
- sparsity inducing
- sparse regression
- low rank
- nuclear norm
- low rank matrix
- accelerated proximal gradient
- missing data
- singular value decomposition
- matrix completion
- linear combination
- convex optimization
- matrix factorization
- high dimensional data
- importance sampling
- semi supervised
- rank minimization
- high order
- kernel matrix
- least squares
- maximum likelihood
- singular values
- high dimensional
- group lasso
- data matrix
- structured sparsity
- variable selection
- data sets
- feature selection and classification
- higher order
- low dimensional
- dimension reduction