In this paper, a novel nonlinear subspace learning technique for class-specific data representation is proposed. A novel data representation is obtained by applying nonlinear class-specific data projection to a discriminant feature space, where the data belonging to the class under consideration are enforced to be close to their class representation, while the data belonging to the remaining classes are enforced to be as far as possible from it. A class is represented by an optimized class vector, enhancing class discrimination in the resulting feature space. An iterative optimization scheme is proposed to this end, where both the optimal nonlinear data projection and the optimal class representation are determined in each optimization step. The proposed approach is tested on three problems relating to human behaviour analysis: face recognition, facial expression recognition and human action recognition. Experimental results denote the effectiveness of the proposed approach, since the proposed Class-specific Reference Discriminant Analysis outperforms Kernel Discriminant Analysis, Kernel Spectral Regression and Class-specific Kernel Discriminant Analysis, as well as Support Vector Machine-based classification, in most cases.
- Class-Specific Kernel Discriminant Analysis
- Class-Specific Kernel Spectral Regression
- Optimized Class Representation
- Human-Computer Interaction