Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery.
Yassir JedraWilliam RéveillardStefan StojanovicAlexandre ProutièrePublished in: CoRR (2024)
Keyphrases
- low rank
- low rank representation
- high dimensional data
- rank minimization
- eigendecomposition
- missing data
- matrix factorization
- affinity matrix
- convex optimization
- linear combination
- regularized regression
- low rank matrix
- singular value decomposition
- matrix completion
- low dimensional
- lower bound
- kernel matrix
- subspace clustering
- trace norm
- semi supervised
- robust principal component analysis
- worst case
- upper bound
- matrix decomposition
- dimensionality reduction
- high dimensional
- high order
- data analysis
- principal component analysis
- data matrix
- nearest neighbor
- data points
- non rigid structure from motion
- singular values
- data sets
- low rank approximation
- feature extraction
- sparse coding
- linear subspace
- subspace learning
- feature space
- image sequences
- image segmentation