کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
533557 870133 2011 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis
چکیده انگلیسی

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.

Research highlights
► Provide a way to design discriminant analysis methods that are bound with classifiers.
► Develop a local mean based nearest neighbor discriminant analysis (LM-NNDA) that matches the LM-NN classifier optimally in theory.
► Show the classical Fisher linear discriminant analysis (FLDA) is a minimum distance classifier induced discriminant analysis method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 44, Issue 7, July 2011, Pages 1387–1402
نویسندگان
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