特征选取和子空间学习是人脸识别的关键问题。为更准确选取人脸中丰富的非线性特征,并解决小样本问题,提出了一种新的L_(2,1)范数正则化的广义核判别分析(generalized kernel discriminant analysis based on L_(2,1)-norm regularizati...特征选取和子空间学习是人脸识别的关键问题。为更准确选取人脸中丰富的非线性特征,并解决小样本问题,提出了一种新的L_(2,1)范数正则化的广义核判别分析(generalized kernel discriminant analysis based on L_(2,1)-norm regularization,L21GKDA)。利用核函数将原始样本隐式地映射到高维特征空间中,得到广义核Fisher鉴别准则,再利用一种有效变换将该非线性模型转化为线性回归模型;为了能使特征选取和子空间学习同时进行,在模型中加入了一种L_(2,1)范数惩罚项,并给出该正则化方法的求解算法。因为方法借助于L_(2,1)范数惩罚项的特征选取能力,所以它能有效地提高识别率。在ORL、AR和PIE人脸库上的实验结果表明,新算法能有效选取人脸的非线性特征,提高判别能力。展开更多
The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discrimin...The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.展开更多
文摘特征选取和子空间学习是人脸识别的关键问题。为更准确选取人脸中丰富的非线性特征,并解决小样本问题,提出了一种新的L_(2,1)范数正则化的广义核判别分析(generalized kernel discriminant analysis based on L_(2,1)-norm regularization,L21GKDA)。利用核函数将原始样本隐式地映射到高维特征空间中,得到广义核Fisher鉴别准则,再利用一种有效变换将该非线性模型转化为线性回归模型;为了能使特征选取和子空间学习同时进行,在模型中加入了一种L_(2,1)范数惩罚项,并给出该正则化方法的求解算法。因为方法借助于L_(2,1)范数惩罚项的特征选取能力,所以它能有效地提高识别率。在ORL、AR和PIE人脸库上的实验结果表明,新算法能有效选取人脸的非线性特征,提高判别能力。
文摘The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.
