Bounds for the Positive Eigenvectors of Nonnegative Matrices and for their Approximations by Decomposition.
Pierre-Jacques CourtoisPierre SemalPublished in: J. ACM (1984)
Keyphrases
- matrix approximation
- theoretical guarantees
- singular value decomposition
- least squares
- eigenvalues and eigenvectors
- eigendecomposition
- lower bound
- approximation methods
- low rank matrix
- low rank matrix approximation
- covariance matrix
- correlation matrix
- positive and negative
- spectral clustering
- data matrix
- upper bound
- affinity matrix
- closed form expressions
- low rank
- stage stochastic programs
- worst case
- eigenvalue problems
- bregman divergences
- principal component analysis
- nonnegative matrix factorization
- singular values
- lower and upper bounds
- decomposition algorithm
- linear programming
- decomposition methods
- pairwise
- contingency tables
- decomposition method
- upper and lower bounds
- efficient computation
- principal components
- closed form
- tensor factorization
- hessian matrix
- proximity matrix
- matrix factorisation