Parallel Feature Extraction through Preserving Global and Discriminative Property for Kernel-Based Image Classification

@inproceedings{Liu2015ParallelFE,
  title={Parallel Feature Extraction through Preserving Global and Discriminative Property for Kernel-Based Image Classification},
  author={Xun-Fei Liu and Xiang-xian Zhu},
  year={2015}
}
Kernel-based feature extraction is widely used in image classification, and different kernel methods extract the features based different criterion. KPCA maximizes the determinant of the total scatter matrix of the transformed sample, while KDA seeks the direction of discrimination. KPCA preserves the global property, and KDA utilizes class information to enhance its discriminative ability so as to perform better than KPCA in classifications. To apply the global property and discriminant… CONTINUE READING