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Based on kernel principal component analysis combined kernel function algorithm

Guoquan Wang

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Abstract

This paper presents a novel combination of kernel function of support vector machine in construction.This combination of kernel functions,Gauss kernel function and polynomial kernel function respective features together,build a both interpolation and extrapolation properties of kernel function.After the experimental verification,the kernel function is applied in the kernel principal component analysis method,which can effectively improve the recognition accuracy and efficiency.

About this research paper

What this paper is about

This paper presents a novel combination of kernel function of support vector machine in construction.This combination of kernel functions,Gauss kernel function and polynomial kernel function respective features together,build a both interpolation and extrapolation properties of kernel function.After the experimental verification,the kernel function is applied in the kernel principal component analysis method,which can effectively improve the recognition accuracy and efficiency.

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Method / approach

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Available abstract

This paper presents a novel combination of kernel function of support vector machine in construction.This combination of kernel functions,Gauss kernel function and polynomial kernel function respective features together,build a both interpolation and extrapolation properties of kernel function.After the experimental verification,the kernel function is applied in the kernel principal component analysis method,which can effectively improve the recognition accuracy and efficiency.

Key concepts: Polynomial kernel, Kernel principal component analysis, Radial basis function kernel, Kernel (algebra), Variable kernel density estimation, Kernel embedding of distributions, Kernel method, Computer science

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