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...
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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.
To improve data distribution efficiency a load-balancing data distribution LBDD method is proposed in publish/subscribe *** the LBDD method subscribers are involved in distribution tasks and data transfers while recei...
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To improve data distribution efficiency a load-balancing data distribution LBDD method is proposed in publish/subscribe *** the LBDD method subscribers are involved in distribution tasks and data transfers while receiving data themselves.A dissemination tree is constructed among the subscribers based on MD5 where the publisher acts as the root. The proposed method provides bucket construction target selection and path updates furthermore the property of one-way dissemination is *** the average out-going degree of a node is 2 is guaranteed with the proposed *** experiments on data distribution delay data distribution rate and load distribution are conducted. Experimental results show that the LBDD method aids in shaping the task load between the publisher and subscribers and outperforms the point-to-point approach.
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