Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain

Shengkun Xie, Anna T. Lawnizak, Pietro Lio, Sridhar Krishnan

Computer Laboratory, University of Cambridge, Cambridge, UK.
Department of Mathematics and Statistics, University of Guelph, Guelph, Canada.
Electrical and Computer Engineering, Ryerson University, Toronto, Canada.
Global Management Studies, Ryerson University, Toronto, Canada.

DOI: 10.4236/eng.2013.510B056                

Abstract

Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.

Keywords

Multi-Scale Principal Component Analysis; Discrete Wavelet Transform; Feature Extraction; Signal Classification; Empirical Classification

Conflicts of Interest

The authors declare no conflicts of interest.

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