MCENN: A variant of extended nearest neighbor method for pattern recognition
Date of Original Version
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.
Publication Title, e.g., Journal
Pattern Recognition Letters
Tang, Bo, Haibo He, and Song Zhang. "MCENN: A variant of extended nearest neighbor method for pattern recognition." Pattern Recognition Letters 133, (2020): 116-122. doi: 10.1016/j.patrec.2020.01.015.