Approximating Matrix Eigenvalues by Subspace Iteration with Repeated Random Sparsification.
Samuel M. GreeneRobert J. WebberTimothy C. BerkelbachJonathan WearePublished in: SIAM J. Sci. Comput. (2022)
Keyphrases
- eigendecomposition
- covariance matrix
- eigenvalues and eigenvectors
- covariance matrices
- singular value decomposition
- singular values
- principal components
- principal component analysis
- correlation matrix
- null space
- signal subspace
- singular vectors
- qr decomposition
- low dimensional
- high dimensional data
- objective function
- low rank
- laplacian matrix
- least squares
- positive definite
- feature extraction
- symmetric matrices
- affinity matrix
- similarity matrix
- transformation matrix
- maximum likelihood
- subspace methods
- pseudo inverse
- positive semi definite
- subspace learning
- sample size
- input data
- high dimensional