Riemannian optimization with subspace tracking for low-rank recovery.
Qian LiWenjia NiuGang LiJianlong TanGang XiongLi GuoPublished in: IJCNN (2016)
Keyphrases
- low rank
- rank minimization
- low rank representation
- high dimensional data
- eigendecomposition
- missing data
- matrix factorization
- convex optimization
- affinity matrix
- linear combination
- singular value decomposition
- matrix completion
- low rank matrix
- semi supervised
- regularized regression
- kernel matrix
- matrix decomposition
- trace norm
- high order
- dimensionality reduction
- low dimensional
- low rank matrices
- manifold learning
- principal component analysis
- convex relaxation
- data analysis
- high dimensional
- collaborative filtering
- active learning
- data sets
- appearance model
- learning algorithm
- small number
- data matrix
- singular values
- human detection
- subspace clustering
- input data