Matrix normal PCA for interpretable dimension reduction and graphical noise modeling.
Chihao ZhangKuo GaiShihua ZhangPublished in: Pattern Recognit. (2024)
Keyphrases
- dimension reduction
- principal component analysis
- singular value decomposition
- dimension reduction methods
- feature extraction
- principle component analysis
- linear discriminant analysis
- high dimensional
- qr decomposition
- low dimensional
- random projections
- feature space
- high dimensional problems
- covariance matrix
- feature selection
- dimensionality reduction
- high dimensionality
- high dimensional data analysis
- high dimensional data
- preprocessing
- manifold learning
- unsupervised learning
- face images
- cluster analysis
- discriminative information
- partial least squares
- neural network
- image processing
- singular values
- feature vectors
- low rank
- data points
- independent component analysis
- discriminant analysis
- least squares
- principal components
- pattern recognition
- manifold embedding
- image data
- learning algorithm