کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
412737 679678 2010 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Reformative nonlinear feature extraction using kernel MSE
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Reformative nonlinear feature extraction using kernel MSE
چکیده انگلیسی

In this paper, we propose an efficient nonlinear feature extraction method using kernel-based minimum squared error (KMSE). This improved method is referred to as reformative KMSE (RKMSE). In RKMSE, we use a linear combination of a small portion of samples that are selected from the training sample set, i.e. “significant nodes”, to approximate to the transform vector of KMSE in kernel space. As a result, RKMSE is much superior to naive KMSE in computational efficiency of feature extraction. Experimental results on several benchmark datasets illustrate that RKMSE can efficiently classify the data with high recognition correct rate.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 73, Issues 16–18, October 2010, Pages 3334–3337
نویسندگان
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