提出了一种特别的特征表示方法,并在此基础上提出了一种基于特别的特征表示方法的局部线性K最近邻算法(locally linear K-nearest neighbor method,L^2KNN),并将之应用到人脸识别中。特别的特征表示方法是在传统的稀疏表示的基础上,加...提出了一种特别的特征表示方法,并在此基础上提出了一种基于特别的特征表示方法的局部线性K最近邻算法(locally linear K-nearest neighbor method,L^2KNN),并将之应用到人脸识别中。特别的特征表示方法是在传统的稀疏表示的基础上,加入了非负约束,改进了传统的稀疏表示的方法,在目标函数中增加了集群正则化项,然后优化新的目标函数得到一个新的近似的特征表示。L^2KNN算法具有最近邻集群效应(clustering effect of nearest neighbors,CENN),不仅可以增强测试样本与同类的训练样本之间的相关性,而且可以增强同类训练样本之间的相关性。L^2KNN算法进一步应用到L^2KNNc(L^2KNN-based classifier)分类器中,并提出一种系数截断的方法增加L^2KNNc分类器的泛化性能,进一步提高分类器的分类性能。在人脸数据集上的实验结果证明了上述结论。展开更多
Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample si...Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.展开更多
文摘提出了一种特别的特征表示方法,并在此基础上提出了一种基于特别的特征表示方法的局部线性K最近邻算法(locally linear K-nearest neighbor method,L^2KNN),并将之应用到人脸识别中。特别的特征表示方法是在传统的稀疏表示的基础上,加入了非负约束,改进了传统的稀疏表示的方法,在目标函数中增加了集群正则化项,然后优化新的目标函数得到一个新的近似的特征表示。L^2KNN算法具有最近邻集群效应(clustering effect of nearest neighbors,CENN),不仅可以增强测试样本与同类的训练样本之间的相关性,而且可以增强同类训练样本之间的相关性。L^2KNN算法进一步应用到L^2KNNc(L^2KNN-based classifier)分类器中,并提出一种系数截断的方法增加L^2KNNc分类器的泛化性能,进一步提高分类器的分类性能。在人脸数据集上的实验结果证明了上述结论。
文摘Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size （SSS） property of face recognition. To solve the two problems,local Bagging （L-Bagging） is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases （AR,Yale,ORL and Yale B） indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation.
