In this paper, we present a new variant of the $k$-nearest neighbor ( $k$NN) classifier based on the maximal margin principle. The proposed method relies on classifying a given unlabeled sample by first finding its $k$-nearest training samples. A local partition of the input feature space is then carried out by means of local support vector machine (SVM) decision boundaries determined after training a multiclass SVM classifier on the considered $k$ training samples. The labeling of the unknown sample is done by looking at the local decision region to which it belongs. The method is characterized by resulting global decision boundaries of the piecewise linear type. However, the entire process can be kernelized through the determination of the $k$-nearest training samples in the transformed feature space by using a distance function simply reformulated on the basis of the adopted kernel. To illustrate the performance of the proposed method, an experimental analysis on three different remote sensing datasets is reported and discussed.
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机译:在本文中,我们提出了基于最大余量原理的$ k $最近邻居(k $ NN)分类器的新变体。所提出的方法依赖于通过首先找到其最近的训练样本来对给定的未标记样本进行分类。然后,通过在考虑的$ k $训练样本上训练多类SVM分类器之后确定的局部支持向量机(SVM)决策边界,对输入特征空间进行局部划分。通过查看未知样本所属的本地决策区域来完成对未知样本的标记。该方法的特征在于产生的分段线性类型的全局决策边界。但是,可以通过使用在所采用内核的基础上简单地重新构造的距离函数,通过确定变换后的特征空间中的最近k个训练样本,来对整个过程进行内核化。为了说明该方法的性能,报告并讨论了对三个不同遥感数据集的实验分析。
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