Forecasting High-Dimensional Covariance Matrices Using High-Dimensional Principal Component Analysis.
Hideto ShigemotoTakayuki MorimotoPublished in: Axioms (2022)
Keyphrases
- high dimensional
- covariance matrix
- covariance matrices
- low dimensional
- principal component analysis
- dimensionality reduction
- similarity search
- feature space
- nearest neighbor
- high dimensional data
- feature extraction
- maximum likelihood
- vector space
- multi class
- gaussian distribution
- gaussian mixture
- input space
- parameter space
- euclidean space
- log euclidean
- image processing
- multivariate normal
- principal components
- sparse coding
- gaussian mixture model
- kernel function
- data points
- lower bound
- objective function
- bayesian networks
- similarity measure