Finding a low-rank basis in a matrix subspace.
Yuji NakatsukasaTasuku SomaAndré UschmajewPublished in: Math. Program. (2017)
Keyphrases
- low rank
- rank minimization
- high dimensional data
- low rank representation
- eigendecomposition
- low rank matrix
- affinity matrix
- missing data
- matrix decomposition
- singular value decomposition
- linear combination
- matrix factorization
- convex optimization
- kernel matrix
- matrix completion
- trace norm
- semi supervised
- subspace clustering
- regularized regression
- high order
- low dimensional
- nuclear norm
- singular values
- dimensionality reduction
- high dimensional
- frobenius norm
- low rank matrices
- nearest neighbor
- low rank and sparse
- factorization methods
- data matrix
- robust principal component analysis
- feature space
- data sets
- kernel matrices
- tensor decomposition
- binary matrix
- low rank approximation
- semidefinite programming
- subspace learning
- principal component analysis
- recommender systems
- pairwise