Sr-LDA:Sparse and Reduced-Rank Linear Discriminant Analysis for High Dimensional Matrix.
Yao WangCheng WangBinyan JiangPublished in: IEEE Signal Process. Lett. (2024)
Keyphrases
- linear discriminant analysis
- high dimensional
- dimensionality reduction
- null space
- high dimensional data
- sparse representation
- small sample size
- discriminant analysis
- feature space
- qr decomposition
- singular value decomposition
- dimension reduction
- low dimensional
- low rank
- face recognition
- singular values
- high dimensionality
- feature extraction
- scatter matrices
- discriminative information
- principal component analysis
- random projections
- sparse coding
- data points
- nearest neighbor
- projection matrix
- subspace learning
- linear discriminant
- fisher criterion
- manifold learning
- support vector
- dealing with high dimensional data
- support vector machine svm
- lower dimensional
- fisher discriminant analysis
- subspace methods
- microarray data
- covariance matrix
- feature selection
- dimensionality reduction methods
- principal components
- scatter matrix
- discriminant information
- supervised dimensionality reduction
- pca lda
- data sets
- involving high dimensional data
- subspace analysis
- high dimensional feature space
- kernel methods
- image classification
- feature vectors
- computer vision