%0 Journal Article
	%A Jun Wan and  Zehua Chen and  Yingwu Chen and  Zhidong Bai
	%D 2010
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 37, 2010
	%T Wavelet and K-L Seperability Based Feature Extraction Method for Functional Data Classification
	%U https://publications.waset.org/pdf/6083
	%V 37
	%X This paper proposes a novel feature extraction method,
based on Discrete Wavelet Transform (DWT) and K-L Seperability
(KLS), for the classification of Functional Data (FD). This method
combines the decorrelation and reduction property of DWT and the
additive independence property of KLS, which is helpful to extraction
classification features of FD. It is an advanced approach of the
popular wavelet based shrinkage method for functional data reduction
and classification. A theory analysis is given in the paper to prove the
consistent convergence property, and a simulation study is also done
to compare the proposed method with the former shrinkage ones. The
experiment results show that this method has advantages in improving
classification efficiency, precision and robustness.
	%P 38 - 44