Eigenvalue Distributions of Sums and Products of Large Random Matrices via Incremental Matrix Expansions
Matthew J. M. PeacockIain B. CollingsMichael L. HonigPublished in: CoRR (2005)
Keyphrases
- perturbation theory
- correlation matrix
- covariance matrix
- covariance matrices
- random matrix theory
- binary matrix
- binary matrices
- singular value decomposition
- random variables
- positive definite
- heavy tailed
- eigenvalues and eigenvectors
- eigenvalue problems
- symmetric matrix
- coefficient matrix
- singular values
- square matrices
- matrix representation
- rows and columns
- probability distribution
- positive semidefinite
- projection matrices
- low rank and sparse
- block diagonal
- random vectors
- pseudo inverse
- symmetric matrices
- factor matrices
- projection matrix
- random samples
- matrix multiplication
- incremental learning
- data matrix
- joint distribution
- low rank matrix
- symmetric positive definite
- eigenvalue decomposition
- linear complementarity problem
- linear algebra
- eigendecomposition
- least squares
- feature space
- maximum likelihood
- distribution function
- sample size
- low rank
- measurement matrix
- semidefinite programming
- sparse matrix
- transformation matrix
- adjacency matrix