Eigenvalue Distributions of Sums and Products of Large Random Matrices Via Incremental Matrix Expansions.
Matthew J. M. PeacockIain B. CollingsMichael L. HonigPublished in: IEEE Trans. Inf. Theory (2008)
Keyphrases
- perturbation theory
- covariance matrix
- covariance matrices
- correlation matrix
- random matrix theory
- binary matrix
- random variables
- binary matrices
- positive definite
- eigenvalue problems
- singular value decomposition
- eigenvalues and eigenvectors
- coefficient matrix
- heavy tailed
- symmetric matrix
- singular values
- matrix representation
- projection matrices
- square matrices
- projection matrix
- probability distribution
- incremental learning
- linear algebra
- random vectors
- eigenvalue decomposition
- pseudo inverse
- factor matrices
- symmetric matrices
- random samples
- sparse matrix
- transformation matrix
- linear complementarity problem
- least squares
- systems of linear equations
- positive semidefinite
- gaussian distribution
- block diagonal
- matrix factorization
- symmetric positive definite
- principal component analysis
- graphical models
- low rank and sparse
- pairwise
- sample size
- distribution function
- marginal distributions
- matrix multiplication
- data matrix
- eigendecomposition
- distance matrix
- null space