MCENN: A variant of extended nearest neighbor method for pattern recognition

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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.

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Pattern Recognition Letters