Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105463
Title: Wavelet and K-L seperability based feature extraction method for functional data classification
Authors: Wan, J.
Chen, Z. 
Chen, Y.
Bai, Z. 
Keywords: Classification
Feature extraction
Functional data
K-l seperability
Wavelet
Issue Date: 2010
Citation: Wan, J., Chen, Z., Chen, Y., Bai, Z. (2010). Wavelet and K-L seperability based feature extraction method for functional data classification. World Academy of Science, Engineering and Technology 61 : 357-363. ScholarBank@NUS Repository.
Abstract: 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.
Source Title: World Academy of Science, Engineering and Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/105463
ISSN: 2010376X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

71
checked on Jan 13, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.