Long-term learning of semantic grouping from relevance-feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Innovative Weighted 2DLDA Approach for Face Recognition
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An intelligent multimodal biometric system for high security access
International Journal of Biometrics
An Innovative Weighted 2DLDA Approach for Face Recognition
Journal of Signal Processing Systems
| Hi-index | 0.00 |
Linear Discriminant Analysis (LDA) is a feature extraction technique for classification. In this paper, we propose a new LDA-based method that can overcome the drawback existed in the traditional LDA methods. It redefines the between-class scatter by adding a weight function according to the between-class distance, which helps to separate the classes as much as possible. At the same time, it projects the between-class scatter into the null space of the within-class scatter that contains the most discriminant information. Hence, the transformationmatrix composed with the eigenvectors corresponding to the largest eigenvalues of the transferred between-class scatter can maximize the Fisher Criteria. Experimental results show our method achieves better performance in comparison with the traditional LDA methods.