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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ADAPTIVE QUASICONFORMAL KERNEL FISHER DISCRIMINANT ANALYSIS VIA WEIGHTED MAXIMUM MARGIN CRITERION
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ADAPTIVE QUASICONFORMAL KERNEL FISHER DISCRIMINANT ANALYSIS VIA WEIGHTED MAXIMUM MARGIN CRITERION

机译：加权最大边际判据的自适应拟形核鱼类判别分析。

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摘要

Kernel Fisher discriminant analysis (KFD) is an effective method to extract nonlinear discriminant features of input data using the kernel trick. However, conventional KFD algorithms endure the kernel selection problem as well as the singular problem. In order to overcome these limitations, a novel nonlinear feature extraction method called adaptive quasiconformal kernel Fisher discriminant analysis (AQKFD) via weighted maximum margin criterion (WMMC) is proposed in this paper. AQKFD, which solves the kernel selection problem, maps the data from the original input space into the quasiconformal kernel mapping space using a quasiconformal kernel. The adaptive parameters of the quasiconformal kernel are calculated through maximizing the measure of class separability of the input data in the quasiconformal kernel mapping space via WMMC which is in terms of the Fisher discriminant criterion. Moreover, when the weight parameter is approximate to the maximum value of Fisher discriminant criterion, then nonlinear features extracted by AQKFD-WMMC have the optimal class separability and AQKFD-WMMC can also solve the singular problem which is endured by KFD. Experimental results on the three real-world datasets, i.e., ORL, YALE and FERET face databases demonstrate the effectiveness of the proposed method.
机译：核Fisher判别分析（KFD）是一种使用核技巧提取输入数据的非线性判别特征的有效方法。然而，常规的KFD算法忍受内核选择问题以及奇异问题。为了克服这些限制，本文提出了一种新的非线性特征提取方法，即通过加权最大余量准则（WMMC）进行的拟拟形核Fisher判别分析（AQKFD）。 AQKFD解决了内核选择问题，它使用准保形内核将数据从原始输入空间映射到准保形内核映射空间。准共形内核的自适应参数是通过根据Fisher判别准则通过WMMC最大化准共形内核映射空间中输入数据的类可分离性的度量来计算的。此外，当权重参数近似于Fisher判别准则的最大值时，则AQKFD-WMMC提取的非线性特征具有最佳的类可分离性，AQKFD-WMMC也可以解决KFD所承受的奇异问题。在三个真实世界的数据集（即ORL，YALE和FERET人脸数据库）上的实验结果证明了该方法的有效性。

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