TITLE:
Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain
AUTHORS:
Shengkun Xie, Anna T. Lawnizak, Pietro Lio, Sridhar Krishnan
KEYWORDS:
Multi-Scale Principal Component Analysis; Discrete Wavelet Transform; Feature Extraction; Signal Classification; Empirical Classification
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
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.