Spectral classification with principal component analysis and artificial neural networks

Abstract

Derived from non-linear signal processing strategies common to biological systems, neural network algorithms generalise classical data analysis techniques, e.g. Fourier analysis, Wiener filtering, and vector clustering algorithms. Conversely, multifactor analysis tools such as principal component analysis can function in a manner analogous to that of an unsupervised neural network. We have explored the use of principal component analysis for data pre-processing prior to classification of stellar spectra with a non-linear neural network. The strategy significantly enhances classification replicability, network stability, and convergence.

Additional details