在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子...在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子集,计算每个子集的中心和半径。当新样本进入时,选择k个决策近邻子集,若其具有相同的类别标签,将该样本标记为相应类别;反之,选择决策近邻子集中最近的k个决策近邻。这种双层结构的加速方式,压缩待测样本的决策近邻规模,提高效率。实验结果表明,KNN_DL方法能够获得较高的样本预测速度和较好的预测准确率。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
文摘在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子集,计算每个子集的中心和半径。当新样本进入时,选择k个决策近邻子集,若其具有相同的类别标签,将该样本标记为相应类别;反之,选择决策近邻子集中最近的k个决策近邻。这种双层结构的加速方式,压缩待测样本的决策近邻规模,提高效率。实验结果表明,KNN_DL方法能够获得较高的样本预测速度和较好的预测准确率。
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.
