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Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification


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Authors

Xie, Shengkun 
Lawniczak, Anna T 
Krishnan, Sridhar 

Abstract

jats:pWe introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.</jats:p>

Description

Keywords

4605 Data Management and Data Science, 46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation

Journal Title

ISRN Computational Mathematics

Conference Name

Journal ISSN

2090-7842
2090-7842

Volume Title

Publisher

Hindawi Limited
Sponsorship
European Commission (257756)