A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate it...A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.展开更多
针对接收信号强度(received signal strength,RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis,KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过...针对接收信号强度(received signal strength,RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis,KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过核函数策略将采集的各接入点(access point,AP)的RSS信号映射到非线性领域,有效提取了非线性定位特征,重组定位信息,去除冗余定位特征和噪声;然后采用LSSVR算法构建指纹点定位特征数据与物理位置的映射关系模型,采用SFLA算法优化该关系模型的参数,并用该关系模型对测试点的位置进行回归预测.实验结果表明:提出算法在相同的采样次数下的定位精度明显优于WKNN,ANN,LSSVR算法,并且在相同的定位精度下,采样次数较大减少,是一种性能良好的WLAN室内定位算法.展开更多
为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA...为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。展开更多
文摘A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.
文摘针对接收信号强度(received signal strength,RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis,KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过核函数策略将采集的各接入点(access point,AP)的RSS信号映射到非线性领域,有效提取了非线性定位特征,重组定位信息,去除冗余定位特征和噪声;然后采用LSSVR算法构建指纹点定位特征数据与物理位置的映射关系模型,采用SFLA算法优化该关系模型的参数,并用该关系模型对测试点的位置进行回归预测.实验结果表明:提出算法在相同的采样次数下的定位精度明显优于WKNN,ANN,LSSVR算法,并且在相同的定位精度下,采样次数较大减少,是一种性能良好的WLAN室内定位算法.
文摘为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。
