Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets.
T. Mitchell RoddenberrySantiago SegarraAnastasios KyrillidisPublished in: CoRR (2020)
Keyphrases
- low rank
- frobenius norm
- matrix completion
- singular values
- low rank approximation
- low rank matrix
- singular value decomposition
- nuclear norm
- convex optimization
- data matrix
- linear combination
- matrix decomposition
- matrix factorization
- missing data
- low rank matrices
- rank minimization
- semi supervised
- low rank and sparse
- eigendecomposition
- kernel matrix
- affinity matrix
- high order
- high dimensional data
- small number
- robust principal component analysis
- trace norm
- kullback leibler divergence
- reconstruction error
- rank aggregation
- norm minimization
- positive semidefinite
- missing values
- binary matrices
- linear programming
- least squares
- data analysis
- data sets