Approximating matrix eigenvalues by randomized subspace iteration.
Samuel M. GreeneRobert J. WebberTimothy C. BerkelbachJonathan WearePublished in: CoRR (2021)
Keyphrases
- eigendecomposition
- covariance matrix
- eigenvalues and eigenvectors
- covariance matrices
- singular value decomposition
- correlation matrix
- principal component analysis
- principal components
- null space
- signal subspace
- singular values
- low dimensional
- low rank
- singular vectors
- laplacian matrix
- dimensionality reduction
- feature space
- qr decomposition
- high dimensional
- projection matrix
- linear subspace
- transformation matrix
- positive definite
- rank minimization
- subspace methods
- linear algebra
- pseudo inverse
- kernel matrix
- subspace clustering
- missing data
- symmetric matrices
- subspace learning
- symmetric matrix
- rows and columns
- decision forest
- face recognition
- lower dimensional
- manifold learning
- spectral clustering
- sample size
- pairwise
- objective function
- similarity measure
- feature extraction