Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression.
Han ChenGarvesh RaskuttiMing YuanPublished in: J. Mach. Learn. Res. (2019)
Keyphrases
- low rank
- convex optimization
- trace norm
- nuclear norm
- high order
- frobenius norm
- tensor decomposition
- low rank matrix
- matrix factorization
- minimization problems
- missing data
- matrix completion
- kernel matrix
- tensor factorization
- linear combination
- matrix decomposition
- singular value decomposition
- rank minimization
- high dimensional data
- semi supervised
- convex relaxation
- interior point methods
- low rank matrices
- regularized regression
- singular values
- kernel matrices
- total variation
- higher order
- semidefinite programming
- objective function
- low rank approximation
- primal dual
- multi task
- loss function
- model selection
- nearest neighbor
- support vector
- face recognition