Title :
Geometrical modification of wavelet SVM kernels and its application in microarray analysis
Author :
Cai, Hong ; Wang, Yufeng
Author_Institution :
Dept. of Biol., Univ. of Texas at San Antonio, San Antonio, TX, USA
Abstract :
The selection and design of appropriate kernel functions play a key role in effective support vector machine (SVM) leaning. A general strategy is to customize the existent kernel functions to fit into the data property and structure. Wavelet kernels have been developed to approximate arbitrary nonlinear functions for signal processing. In this paper, we propose novel wavelet kernels based on the Riemannian geometrical structure theory, by constructing a hyperplane with better spatial resolution. This wavelet kernel SVM approach was applied to the yeast time course microarray dataset and outperformed the traditional Gaussian kernel and polynomial kernel.
Keywords :
biology computing; computational geometry; data analysis; data structures; learning (artificial intelligence); support vector machines; wavelet transforms; Gaussian kernel; Riemannian geometrical structure theory; arbitrary nonlinear functions; data property; data structure; effective support vector machine leaning; geometrical modification; kernel functions; microarray analysis; polynomial kernel; signal processing; wavelet SVM kernels; yeast time course microarray dataset; Kernel; Polynomials; Signal processing; Spatial resolution; Support vector machines; Wavelet analysis; Wavelet transforms;
Conference_Titel :
Genomic Signal Processing and Statistics (GENSIPS), 2011 IEEE International Workshop on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4673-0491-7
Electronic_ISBN :
2150-3001
DOI :
10.1109/GENSiPS.2011.6169467