Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28539
Title: A theoretical analysis on nearest feature space classifier
Authors: Zhang, H
Wang, K
Zhang, D 
Niu, X
Zuo, W
Chen, Y
Keywords: Classifier design
Image recognition
Nearest feature line (NFL)
Nearest feature space (NFS)
Issue Date: 2006
Publisher: WSEAS Press
Source: WSEAS transactions on information science and applications, 2006, v. 3, no. 4, p. 677-683 How to cite?
Journal: WSEAS transactions on information science and applications 
Abstract: Classifier design is an important issue in pattern recognition. Nearest Feature Line (NFL) classifier had been proposed to enhance the prototype-representing capacity of nearest neighbor methods. Nearest Feature Space (NFS) is further proposed as a generalization of NFL. In this paper we give a formal definition and theoretical analysis on Feature Space. First, the dimensionality and coordinate origin problems in classical NFS are presented. Then, a novel NFS classifier is designed to solve the above-mentioned problems. For contrastive analysis, a case study on image recognition is carried out by using ORL database. Experimental results indicate that the proposed NFS classifier offers a better recognition performance than classical NFS, which practically proves that the proposed NFS accommodates an outstanding prototype-representing capacity.
URI: http://hdl.handle.net/10397/28539
ISSN: 1790-0832
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