ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]
Document Type
Article
Date of Original Version
8-1-2015
Abstract
This article introduces a new supervised classification method-the extended nearest neighbor (ENN)-that predicts input patterns according to the maxiμm gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a two-way communication style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.
Publication Title, e.g., Journal
IEEE Computational Intelligence Magazine
Volume
10
Issue
3
Citation/Publisher Attribution
Tang, Bo, and Haibo He. "ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]." IEEE Computational Intelligence Magazine 10, 3 (2015): 52-60. doi: 10.1109/MCI.2015.2437512.