Recent studies have shown that extended nearest neighbor (ENN) method is able to improve the classification performance over traditional k-nearest neighbor (KNN) methods, due to its novel “two-way communication” decision making process. The ENN method classifies a test data sample by maximizing the intra-class coherence gain over the whole data set. However, the original ENN method, like KNN, is a type of instance-based learning algorithm without a training stage. This paper presents a variant of ENN method, called Maximum intra-class Coherence Extended Nearest Neighbor (MCENN), which incorporates distances between individual data sample and its nearest neighbors into the decision making process and introduces a novel distance metric learning algorithm as a training process to learn an optimal linear transformation that maximizes the overall intra-class coherence of training data. We demonstrate that the proposed MCENN approach is able to improve the discriminative performance for pattern recognition. Experimental results on real-life data sets demonstrate the effectiveness of the proposed approaches.

最近的研究表明，扩展的最近邻（ENN）方法由于其新颖的“双向通信”决策过程，能够比传统的k近邻（KNN）方法提高分类性能。ENN方法通过最大化整个数据集的类内相干增益来对测试数据样本进行分类。但是，原始的ENN方法（如KNN）是一种基于实例的学习算法，没有训练阶段。本文呈现ENN方法的变体，被称为中号aximum类内Ç oherence Ë xtended Ñ earest Ñ邻居（MCENN），它将单个数据样本与其最近邻居之间的距离纳入决策制定过程，并引入一种新颖的距离度量学习算法作为训练过程，以学习最佳线性变换，从而最大化训练数据的整体类内一致性。我们证明了提出的MCENN方法能够提高模式识别的判别性能。真实数据集上的实验结果证明了所提出方法的有效性。