کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
411202 679184 2007 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Kernel based symmetrical principal component analysis for face classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Kernel based symmetrical principal component analysis for face classification
چکیده انگلیسی

Kernel method is a powerful technique in machine learning and it has been widely applied to feature extraction and classification. Symmetrical principal component analysis (SPCA) is an excellent feature extraction method for face classification because it utilizes the symmetry of the facial images. This paper presents one Kernel based SPCA (KSPCA) algorithm which gives the closed form for polynomial kernel. KSPCA combines advantages of SPCA with kernel method, i.e., KSPCA not only makes use of the symmetry of the facial images, but also extracts nonlinear principal components which contain more abundant information. We compare the performance of SPCA, kernel PCA (KPCA) with KSPCA on CBCL database for binary classification, and on ORL and Yale face database for multi-category classification, respectively. The experimental results show that KSPCA outperforms both SPCA and KPCA.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 4–6, January 2007, Pages 904–911
نویسندگان
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