An information rich subspace separation for non-stationary signal classification

Abstract

This paper proposes a novel automated approach for non-principal component analysis (non-PCA) methodology. This method generates an eigen analysis based pseudospectrum to emulate the spectral characteristic variations of pseudo-spectrum is used to implement a comb like subspace spectral components throughout the whole observation period. results than the existing dimensionality reduction methods, which only utilise the principal k components of the eigen space. Finally, a novel probabilistic approach which creates a signature vector representing each class of signals in the training that the proposed method can be effectively used not only for signature features from a non-stationary signal. Further, it is be used as a dynamic spectral estimation technique, which can eliminate the time frequency resolution tradeoff that exists in techniques such as short-time fourier transform (STFT).