Non-negative low-rank adaptive preserving sparse matrix regression model for supervised image feature selection and classification.
Xiuhong ChenXingyu ZhuYun LuZhifang PuPublished in: IET Image Process. (2023)
Keyphrases
- regression model
- low rank
- sparse regression
- feature selection and classification
- sparse matrix
- input image
- semi supervised
- image classification
- image features
- singular values
- convex optimization
- singular value decomposition
- missing data
- high resolution
- machine learning
- matrix factorization
- similarity measure
- image representation
- unsupervised learning
- feature extraction
- neural network
- supervised learning
- feature selection
- learning algorithm
- pattern recognition