  In this paper we explore unsupervised spectral decomposition methods for
distinguishing the effect of different spectral components for a set of
consecutive spectra from an X-ray binary. We use well-established linear
methods for the decomposition, namely principal component analysis, independent
component analysis and non-negative matrix factorisation (NMF). Applying these
methods to a simulated dataset consisting of a variable multicolour disc black
body and a cutoff power law, we find that NMF outperforms the other two methods
in distinguishing the spectral components. In addition, due the non-negative
nature of NMF, the resulting components may be fitted separately, revealing the
evolution of individual parameters. To test the NMF method on a real source, we
analyse data from the low-mass X-ray binary GX 339-4 and found the results to
match those of previous studies. In addition, we found the inner radius of the
accretion disc to be located at the innermost stable circular orbit in the
intermediate state right after the outburst peak. This study shows that using
unsupervised spectral decomposition methods results in detecting the separate
component fluxes down to low flux levels. Also, these methods provide an
alternative way of detecting the spectral components without performing actual
spectral fitting, which may prove to be practical when dealing with large
datasets.
