A New 3-D PCA Regression Method for Manifold Dimension Reduction with Image Analysis.
Kyung Min LeeChi-Ho LinPublished in: ICEIC (2023)
Keyphrases
- dimension reduction
- regression method
- partial least squares
- principal component analysis
- manifold learning
- low dimensional
- nonlinear manifold
- manifold learning algorithm
- manifold embedding
- lower dimensional
- principle component analysis
- high dimensional
- dimensionality reduction
- generative topographic mapping
- feature space
- feature extraction
- linear discriminant analysis
- random projections
- high dimensional data
- dimension reduction methods
- singular value decomposition
- kernel function
- face recognition
- computer vision
- pattern recognition
- principal components
- linear regression
- independent component analysis
- discriminant analysis
- linear subspace
- variable selection
- cluster analysis
- image processing
- unsupervised learning
- regression model
- locally linear embedding
- high dimensionality
- regression analysis
- knn
- kernel pca
- principal components analysis
- data points
- feature selection
- microarray
- preprocessing
- image segmentation