In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the d...In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.展开更多
The quantitative analysis of three-dimensional (3-D) human blood vessel structuresplays a very important role in the clinical diagnosis. The conventional X-ray approacheshave some shortcomings, such as the need to mak...The quantitative analysis of three-dimensional (3-D) human blood vessel structuresplays a very important role in the clinical diagnosis. The conventional X-ray approacheshave some shortcomings, such as the need to make use of a dye-product and basisprojective-integrative rule for the image formation. On the one hand, the patient hasto suffer a great radiation dose, and a registration process is also often needed to cor-rect the displacement bias between the images due to the patient’s movements. On展开更多
基金The National Natural Science Foundation of China(No.6120134461271312+7 种基金6140108511301074)the Research Fund for the Doctoral Program of Higher Education(No.20120092120036)the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031)Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11)"333"Project(No.BRA2015288)High-End Foreign Experts Recruitment Program(No.GDT20153200043)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
文摘The quantitative analysis of three-dimensional (3-D) human blood vessel structuresplays a very important role in the clinical diagnosis. The conventional X-ray approacheshave some shortcomings, such as the need to make use of a dye-product and basisprojective-integrative rule for the image formation. On the one hand, the patient hasto suffer a great radiation dose, and a registration process is also often needed to cor-rect the displacement bias between the images due to the patient’s movements. On
