Nonlinear Dimensionality Reduction Via Polynomial Principal Component Analysis.
Abbas KazemipourShaul DruckmannPublished in: GlobalSIP (2018)
Keyphrases
- nonlinear dimensionality reduction
- principal component analysis
- dimensionality reduction
- low dimensional
- dimensionality reduction methods
- manifold learning
- locally linear embedding
- high dimensional data
- dimension reduction
- laplacian eigenmaps
- principal components
- high dimensional
- kernel pca
- maximum variance unfolding
- feature extraction
- random projections
- covariance matrix
- discriminant analysis
- subspace learning
- face recognition
- linear discriminant analysis
- vector space
- high dimensionality
- face images
- unsupervised learning
- data sets
- euclidean distance
- preprocessing step
- sparse representation
- principal components analysis
- distance measure
- feature set
- feature space
- pattern recognition
- machine learning