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has been cited by the following article:

• 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.