Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain
Shengkun Xie, Anna T. Lawnizak, Píetro Lió, Sridhar Krishnan · Engineering · 2013
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.