کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532393 869947 2012 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A new discriminant subspace analysis approach for multi-class problems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A new discriminant subspace analysis approach for multi-class problems
چکیده انگلیسی

Fukunaga–Koontz Transform (FKT) is a famous feature extraction method in statistical pattern recognition, which aims to find a set of vectors that have the best representative power for one class while the poorest representative power for the other class. Li and Savvides [1] propose a one-against-all strategy to deal with multi-class problems, in which the two-class FKT method can be directly applied to find the presentative vectors of each class. Motivated by the FKT method, in this paper we propose a new discriminant subspace analysis (DSA) method for the multi-class feature extraction problems. To solve DSA, we propose an iterative algorithm for the joint diagonalization (JD) problem. Finally, we generalize the linear DSA method to handle nonlinear feature extraction problems via the kernel trick. To demonstrate the effectiveness of the proposed method for pattern recognition problems, we conduct extensive experiments on real data sets and show that the proposed method outperforms most commonly used feature extraction methods.


► We propose a new DSA method for multi-class feature extraction problems.
► We propose an iterative algorithm for solving joint diagonalization problem.
► We generalize DSA via kernel trick to handle nonlinear feature extraction problems.
► We conduct extensive experiments to confirm the effectiveness of proposed methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 45, Issue 4, April 2012, Pages 1426–1435
نویسندگان
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