Orthogonal LDA in PCA Transformed Subspace.
M. Mahadeva PrasadM. SukumarA. G. RamakrishnanPublished in: ICFHR (2010)
Keyphrases
- principal component analysis
- linear discriminant analysis
- subspace methods
- dimensionality reduction
- principal components analysis
- linear discriminate analysis
- subspace analysis
- discriminant vectors
- face recognition
- low dimensional
- discriminant analysis
- discriminant projection
- feature extraction
- discriminative subspace
- principal components
- dimension reduction
- feature space
- subspace learning
- methods such as principal component analysis
- high dimensional data
- lower dimensional
- scatter matrices
- independent component analysis
- null space
- covariance matrix
- kernel pca
- linear projection
- pca lda
- principle component analysis
- kernel principal component analysis
- discriminant information
- small sample size
- locality preserving projections
- qr decomposition
- linear subspace
- face images
- scatter matrix
- data sets
- subspace learning algorithm
- support vector
- manifold learning
- high dimensional
- support vector machine svm
- semi supervised
- pattern recognition
- dimensionality reduction methods
- random projections
- face databases
- ensemble methods
- high dimensionality
- singular value decomposition
- gabor features