| Paper: | MLSP-P5.2 | ||
| Session: | Image and Video Processing | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Image and Video Processing Applications | ||
| Title: | MODIFIED KERNEL-BASED NONLINEAR FEATURE EXTRACTION | ||
| Authors: | Guang Dai; Zhejiang University | ||
| Yuntao Qian; Zhejiang University | |||
| Sen Jia; Zhejiang University | |||
| Abstract: | Feature extraction techniques are widely used in many applications to pro-process data in order to reduce the complexity of subsequent processes. A group of kernel-based nonlinear Fisher discrminant analysis (KFDA) has attracted much attention due to their high performance. In this paper, the inherent limitations of those KFDA algorithms have been discussed and the novel algorithm will be proposed to effectively overcome those limitations. Experimental results on the face recognition suggest that this proposed algorithm is superior to the existing methods in terms of correct classification rate. | ||
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