Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings.
Anestis TouloumisPublished in: Comput. Stat. Data Anal. (2015)
Keyphrases
- covariance matrix
- high dimensional
- covariance matrices
- principal component analysis
- geometrical interpretation
- sample size
- gaussian mixture
- pseudo inverse
- dimensionality reduction
- low dimensional
- mahalanobis distance
- positive definite
- correlation matrix
- similarity search
- parameter space
- eigenvalues and eigenvectors
- objective function
- eigendecomposition
- symmetric matrix
- computer vision
- mahalanobis metric
- random projections
- principal components
- evolutionary algorithm
- lower bound