Finding a low-rank basis in a matrix subspace.
Yuji NakatsukasaTasuku SomaAndré UschmajewPublished in: CoRR (2015)
Keyphrases
- low rank
- rank minimization
- high dimensional data
- low rank representation
- eigendecomposition
- singular value decomposition
- affinity matrix
- convex optimization
- matrix completion
- matrix factorization
- low rank matrix
- missing data
- matrix decomposition
- linear combination
- trace norm
- semi supervised
- regularized regression
- low dimensional
- high order
- kernel matrix
- singular values
- data matrix
- factorization methods
- subspace clustering
- robust principal component analysis
- nuclear norm
- dimensionality reduction
- high dimensional
- low rank matrices
- frobenius norm
- low rank and sparse
- data analysis
- tensor decomposition
- principal component analysis
- pattern recognition
- nearest neighbor
- machine learning
- optical flow
- missing values
- least squares
- higher order
- subspace learning
- original data