Title
MCENN: A variant of extended nearest neighbor method for pattern recognition
Document Type
Article
Date of Original Version
5-1-2020
Abstract
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
Volume
133
Citation/Publisher Attribution
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.