Dimensional Reduction of Large Image Datasets Using Non-linear Principal Components.
Silvia Silva da Costa BotelhoWillian LautenschlgerMatheus Bacelo de FigueiredoTania Mezzadri CentenoMauricio M. MataPublished in: IDEAL (2005)
Keyphrases
- dimensional reduction
- principal components
- principal component analysis
- dimensionality reduction
- low dimensional
- feature space
- feature extraction
- face recognition
- dimension reduction
- locally linear embedding
- discriminant analysis
- principal components analysis
- high dimensional data
- subspace learning
- spectral data
- kernel space
- covariance matrix
- linear discriminant analysis
- neural network
- euclidean distance
- sparse representation
- dimensionality reduction methods
- high dimensional
- pattern recognition
- data sets
- manifold learning
- preprocessing step
- data mining
- principal component regression