A Hybrid Dimension Reduction Based Linear Discriminant Analysis for Classification of High-Dimensional Data.
Ezgi ZorarpaciPublished in: CEC (2021)
Keyphrases
- dimension reduction
- high dimensional data
- linear discriminant analysis
- dimensionality reduction
- small sample size
- high dimensionality
- low dimensional
- discriminant analysis
- discriminative information
- high dimensional
- nearest neighbor
- manifold learning
- principal component analysis
- feature extraction
- similarity search
- data sets
- data points
- original data
- support vector machine svm
- random projections
- input space
- support vector
- lower dimensional
- data analysis
- principle component analysis
- face recognition
- sparse representation
- pattern recognition
- subspace clustering
- principal components analysis
- null space
- feature space
- subspace learning
- subspace methods
- qr decomposition
- high dimensional data analysis
- singular value decomposition
- training samples
- unsupervised learning
- machine learning
- cluster analysis
- k nearest neighbor
- input data
- least squares
- locally linear embedding
- feature vectors
- preprocessing
- similarity measure
- scatter matrices
- feature selection