An approximate polynomial matrix eigenvalue decomposition algorithm for para-Hermitian matrices.
Soydan RedifStephan WeissJohn G. McWhirterPublished in: ISSPIT (2011)
Keyphrases
- decomposition algorithm
- singular values
- symmetric matrix
- perturbation theory
- square matrices
- positive semidefinite
- singular value decomposition
- symmetric matrices
- covariance matrix
- working set
- semidefinite programming
- eigenvalue problems
- correlation matrix
- decomposition method
- low rank
- projection matrix
- eigenvalue decomposition
- positive definite
- coefficient matrix
- kernel matrix
- linear combination
- equality constraints
- matrix completion
- least squares
- eigenvalues and eigenvectors
- working set selection
- metric learning
- data matrix
- matrix representation
- polynomial equations
- projection matrices
- sparse matrices
- semidefinite
- pseudo inverse
- decomposition methods
- systems of linear equations
- missing data
- semi supervised
- rows and columns
- distance matrix