Handling high dimensionality in biometric classification with multiple quality measures using Locality Preserving Projection.
Krzysztof KryszczukNorman PohPublished in: CVPR Workshops (2010)
Keyphrases
- high dimensionality
- quality measures
- dimensionality reduction
- feature space
- locality preserving projections
- feature selection
- high dimensional
- small sample size
- high dimensional data
- pattern recognition
- microarray
- class imbalance
- dimension reduction
- feature extraction
- unsupervised learning
- classification accuracy
- neural network
- training samples
- low dimensional
- benchmark datasets
- support vector machine
- sparse coding
- feature vectors
- pairwise
- support vector
- image classification
- manifold learning
- high dimensional spaces
- dimensionality reduction methods
- locally linear embedding
- machine learning