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

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DOI: 10.4236/eng.2013.510B056    3,257 Downloads   4,338 Views   Citations

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.

Cite this paper

Xie, S. , Lawnizak, A. , Lio, P. and Krishnan, S. (2013) Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain. Engineering, 5, 268-271. doi: 10.4236/eng.2013.510B056.

Conflicts of Interest

The authors declare no conflicts of interest.

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