A powerful method for feature extraction and compression of electronic nose responses

This paper focuses on the problem of data representation for feature selection and extraction of 1D electronic nose signals. While PCA
signal representation is a problem dependent method, we propose a novel approach based on frame theory where an over-complete dictionary
of functions is considered in order to find the near-optimal representation of any 1D signal considered. Feature selection is accomplished with
an iterative methods called matching pursuit which select from the dictionary the functions that reduce the reconstruction error. In this case
we can use the representation functions found for feature extraction or for signal compression purposes. Classification results of the selected
features is performed with neural approach showing the high discriminatory power of the extracted feature.