Nonlinear Dimensionality Reduction in Texture Classification: Is Manifold Learning Better Than PCA?
Cédrick Bamba NsimbaAlexandre L. M. LevadaPublished in: ICCS (5) (2019)
Keyphrases
- texture classification
- manifold learning
- nonlinear dimensionality reduction
- feature extraction
- dimensionality reduction
- principal component analysis
- low dimensional
- dimension reduction
- locally linear embedding
- laplacian eigenmaps
- high dimensional
- diffusion maps
- principal components analysis
- subspace learning
- principal components
- texture features
- high dimensional data
- face recognition
- feature space
- dimensionality reduction methods
- linear discriminant analysis
- pattern recognition
- random projections
- discriminant analysis
- face images
- low dimensional manifolds
- image processing
- manifold structure
- preprocessing
- feature vectors
- image classification
- preprocessing step
- data points
- covariance matrix
- feature selection
- high dimensionality
- nearest neighbor
- data sets
- support vector machine svm
- k means
- data mining