Provider: ingentaconnect
Database: ingentaconnect
Content: application/x-research-info-systems
TY - ABST
AU - Wangen, L. E.
AU - Frew, N. M.
AU - Isenhour, T. L.
AU - Jurs, P. C.
TI - Investigation of the Fourier Transform for Analyzing Spectroscopic Data by Computerized Learning Machines
JO - Applied Spectroscopy
PY - 1971-03-01T00:00:00///
VL - 25
IS - 2
SP - 203
EP - 207
KW - Pattern classification, Fast Fourier transform, Computerized learning machines, Analyses of mass spectra
N2 - This paper investigates the use of the fast Fourier transform as an aid in the analysis and classification of spectroscopic data. The pattern obtained after transformation is viewed as a weighted average and/or as a frequency representation of the original spectroscopic data. In pattern
recognition the Fourier transform allows a different (i.e., a frequency) representation of the data which may prove more amenable to linear separation according to various categories of the patterns. The averaging property means that the information in each dimension of the original pattern
is distributed over all dimensions in the pattern resulting from the Fourier transformation. Hence the arbitrary omission or loss of data points in the Fourier spectrum has less effect on the original spectrum. This property is exploited for reducing the dimensionality of the Fourier data
so as to minimize data storage requirements and the time required for development of pattern classifiers for categorization of the data. Examples of applications are drawn from low resolution mass spectrometry.
UR - http://www.ingentaconnect.com/content/sas/sas/1971/00000025/00000002/art00008
M3 - doi:10.1366/000370271779948736
UR - http://dx.doi.org/10.1366/000370271779948736
ER -