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中文摘要：
Fisher鉴别分析被公认为是特征抽取的有效方法之一,但由于其只能抽取线性特征,而对于实际应用中复杂的样本图像分布,抽取非线性鉴别特征显得十分必要。先前的基于核Fisher鉴别分析算法虽然解决了非线性特征抽取问题,但是其存在最终特征维数受类别数限制的问题。为了能够进一步提高特征提取效率,提出了一种基于核的Fisher极小鉴别分析方法,该方法使得最终特征维数不受类别数限制。在Yale和NUST603人脸库上进行了鉴别性能实验,实验结果验证了该方法的有效性。
英文摘要：
Linear （Fisher） discriminant analysis （LDA） is a well known and popular statistical method for feature extraction,but,due to its limitation of linearity,it fails to perform well for nonlinear problems in a lot of real-world applications,so it is necessary to extract nonlinear features. Though the conventional kernel Fisher discriminant analysis has overcome the nonlinear problems,the limitation of final eigenvectors’dimensions determined by class number still exists. To extract more effective classification information,a method of kernel-based Fisher minimum discriminant analysis was proposed. The proposed one overcomes the limitation of final eigenvectors’dimensions determined by class number. The results of experiments conducted on Yale and NUST603 face databases show the effectiveness of the proposed algorithm.
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同项目期刊论文
An approach for directly extracting features from matrix data and its application in face recognitio
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to F
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