کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10360389 869792 2014 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Enhanced iterative projection for subclass discriminant analysis under EM-alike framework
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Enhanced iterative projection for subclass discriminant analysis under EM-alike framework
چکیده انگلیسی
Mixture discriminant analysis (MDA) and subclass discriminant analysis (SDA) belong to the supervised classification approaches. They have advantage over the standard linear discriminant analysis (LDA) in large sample size problems, since both of them divide the samples in each class into subclasses which keep locality but LDA does not. However, since the current MDA and SDA algorithms perform subclass division in just one step in the original data space before solving the generalized eigenvalue problem, two problems are exposed: (1) they ignore the relation among classes since subclass division is performed in each isolated class; (2) they cannot guarantee good performance of classifiers in the transformed space, because locality in the original data space may not be kept in the transformed space. To address these problems, this paper presents a new approach for subclass division based on k-means clustering in the projected space, class by class using the iterative steps under EM-alike framework. Experiments are performed on the artificial data set, the UCI machine learning data sets, the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, and the terrain cover database. Extensive experimental results demonstrate the performance advantages of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 47, Issue 3, March 2014, Pages 1113-1125
نویسندگان
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