A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis.
Hao JiangDaniel P. RobinsonRené VidalChong YouPublished in: Comput. Optim. Appl. (2018)
Keyphrases
- low rank
- convergence analysis
- convex optimization
- low rank matrices
- robust principal component analysis
- high dimensional data
- global convergence
- matrix completion
- missing data
- kernel matrix
- low rank matrix
- subspace clustering
- linear combination
- matrix factorization
- optimality conditions
- singular value decomposition
- rank minimization
- semi supervised
- nonlinear programming
- high order
- semidefinite programming
- total variation
- feature selection
- convergence rate
- objective function
- primal dual
- approximation methods
- clustering algorithm
- learning algorithm
- quadratic programming
- training set
- loss function
- low dimensional
- linear programming
- optimization problems