Adaptive quasiconformal kernel discriminant analysis

作者：Pan, Jeng-Shyang; Li, Jun-Bao^*; Lu, Zhe-Ming

来源：Neurocomputing, 2008, 71(13-15): 2754-2760.

DOI：10.1016/j.neucom.2007.10.003

• kernel method
• quasiconformal kernel
• kernel discriminant analysis
• adaptive quasiconformal kernel discriminant analysis
• feature extraction

Kernel discriminant analysis (KDA) is effective to extract nonlinear discriminative features of input samples using the kernel trick. However, the conventional KDA algorithm endures the kernel selection which has significant impact on the performances of KDA. In order to overcome this limitation, a novel nonlinear feature extraction method called adaptive quasiconformal kernel discriminant analysis (AQKDA) is proposed in this paper. AQKDA maps the data from the original input space to the high dimensional kernel space using a quasiconformal kernel. The adaptive parameters of the quasiconformal kernel are automatically calculated through optimizing an objective function designed for measuring the class separability of data in the feature space. Consequently, the nonlinear features extracted by AQKDA have the larger class separability compared with KDA. Experimental results on the two real-world datasets demonstrate the effectiveness of the proposed method.

• 出版日期2008-8
• 单位中山大学; 哈尔滨工业大学