Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24266
Title: From classifiers to discriminators : a nearest neighbor rule induced discriminant analysis
Authors: Yang, J
Zhang, L 
Yang, JY
Zhang, D 
Keywords: Classification
Classifier
Dimensionality reduction
Discriminant analysis
Feature extraction
Pattern recognition
Issue Date: 2011
Publisher: Elsevier
Source: Pattern recognition, 2011, v. 44, no. 7, p. 1387-1402 How to cite?
Journal: Pattern recognition 
Abstract: The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose. This paper provides a way to design discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a discriminator. Therefore, the derived discriminator, called local mean based nearest neighbor discriminant analysis (LM-NNDA), matches the LM-NN classifier optimally in theory. In contrast to that LM-NNDA is a NN classifier induced discriminant analysis method, we further show that the classical Fisher linear discriminant analysis (FLDA) is a minimum distance classifier (i.e. nearest Class-mean classifier) induced discriminant analysis method. The proposed LM-NNDA method is evaluated using the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, the ETH80 object category database and the FERET face image database. The experimental results demonstrate the performance advantage of LM-NNDA over other feature extraction methods with respect to the LM-NN (or NN) classifier.
URI: http://hdl.handle.net/10397/24266
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2011.01.009
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