Phase-I monitoring of high-dimensional covariance matrix using an adaptive thresholding LASSO rule.
Galal M. AbdellaMohammad Reza MalekiSangahn KimKhalifa N. Al-KhalifaAbdel Magid S. HamoudaPublished in: Comput. Ind. Eng. (2020)
Keyphrases
- covariance matrix
- high dimensional
- variable selection
- sample size
- covariance matrices
- principal component analysis
- mahalanobis distance
- generalized linear models
- low dimensional
- dimensionality reduction
- gaussian mixture
- phase ii
- correlation matrix
- objective function
- principal components
- dimension reduction
- feature selection
- nearest neighbor
- symmetric matrix
- cross validation
- linear regression
- similarity search
- feature space
- class conditional densities
- sparse representation
- estimation error
- high dimensional data
- edge detection
- denoising
- image segmentation
- multivariate gaussian
- positive definite
- geometrical interpretation
- data points
- pseudo inverse
- least squares
- genetic algorithm
- regression model
- parameter space