Dimensionality reduction by collaborative preserving Fisher discriminant analysis.
Ming-Dong YuanDa-Zheng FengYa ShiWen-Juan LiuPublished in: Neurocomputing (2019)
Keyphrases
- fisher discriminant analysis
- dimensionality reduction
- dimensionality reduction methods
- kernel trick
- supervised dimensionality reduction
- principal component analysis
- locality preserving projections
- discriminant analysis
- linear discriminant analysis
- high dimensional data
- low dimensional
- feature extraction
- locality preserving
- high dimensional
- linear transformation
- lower dimensional
- factor analysis
- preprocessing step
- input space
- high dimensionality
- manifold learning
- data representation
- dimension reduction
- subspace learning
- locally linear embedding
- pattern recognition
- principal components
- random projections
- multiple kernel learning
- feature selection
- data sets
- data points
- feature space
- metric learning
- kernel pca
- high dimensional feature space
- machine learning
- face recognition
- manifold structure
- knn
- distance measure