A theoretical analysis on nearest feature space classifier

Hongzhi Zhang, Kuanquan Wang, Dapeng Zhang, Xiamu Niu, Wangmeng Zuo, Yan Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Pages (from-to)677-683
Number of pages7
JournalWSEAS Transactions on Information Science and Applications
Volume3
Issue number4
Publication statusPublished - 1 Apr 2006

Keywords

  • Classifier design
  • Image recognition
  • Nearest feature line (NFL)
  • Nearest feature space (NFS)

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications

Cite this