GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis.
Florian HeinrichsPublished in: CoRR (2024)
Keyphrases
- principal component analysis
- dimensionality reduction
- principal components
- low dimensional
- dimension reduction
- linear discriminant analysis
- independent component analysis
- face images
- high dimensional data
- special case
- lower dimensional
- feature extraction
- dimensional reduction
- face recognition
- linear dimensionality reduction
- covariance matrix
- closely related
- feature space
- kernel pca
- high dimensional
- dimensionality reduction methods
- discriminant analysis
- linear projection
- high quality
- subspace learning
- least squares
- structure preserving
- manifold learning
- input space
- random projections
- kernel principal component analysis
- discriminant projection
- face databases
- high dimensionality
- pattern recognition
- negative matrix factorization
- metric learning
- nonlinear dimensionality reduction
- principle component analysis
- original data
- training set
- pattern recognition and machine learning
- feature selection