Low-rank nonnegative sparse representation and local preservation-based matrix regression for supervised image feature selection.
Xingyu ZhuXiuhong ChenPublished in: IET Image Process. (2021)
Keyphrases
- low rank
- sparse representation
- low rank matrix recovery
- image representation
- matrix decomposition
- image classification
- singular values
- reconstruction error
- high dimensional data
- low rank matrix
- feature selection
- singular value decomposition
- sparsity constraints
- semi supervised
- missing data
- matrix factorization
- matrix completion
- data matrix
- linear combination
- convex optimization
- sparse coding
- multiscale
- input image
- dimensionality reduction
- rank minimization
- image features
- image data
- unsupervised feature selection
- nonnegative matrix factorization
- dictionary learning
- norm minimization
- low rank and sparse
- unsupervised learning
- feature extraction
- compressive sensing
- kernel matrix
- feature space
- high resolution
- object recognition
- negative matrix factorization
- face recognition
- orthogonal matching pursuit
- high order
- image set
- natural images
- signal processing
- subspace learning
- compressed sensing
- image patches
- higher order
- collaborative filtering
- supervised learning
- nearest neighbor
- image retrieval
- high dimensional
- image segmentation