کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
411216 | 679184 | 2007 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A method for speeding up feature extraction based on KPCA
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Kernel principal component analysis (KPCA) extracts features of samples with an efficiency in inverse proportion to the size of the training sample set. In this paper, we develop a novel method to improve KPCA-based feature extraction. The developed method is the first one that is methodologically consistent with KPCA. Experiments on several benchmark datasets illustrate that the feature extraction process derived from the novel method is much more efficient than that associated with KPCA. Moreover, the classification accuracy generated from the developed method is similar to that of KPCA.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 4–6, January 2007, Pages 1056–1061
Journal: Neurocomputing - Volume 70, Issues 4–6, January 2007, Pages 1056–1061
نویسندگان
Yong Xu, David Zhang, Fengxi Song, Jing-Yu Yang, Zhong Jing, Miao Li,