  This paper introduces the use of Principal Component Analysis as a method to
decompose the waveform catalogues to produce a set of orthonormal basis
vectors. We apply this method to a set of supernova waveforms and compare the
basis vectors obtained with those obtained through Gram-Schmidt decomposition.
We observe that, for the chosen set of waveforms, the performance of the two
methods are comparable for minimal match requirements up to 0.9, with 14
Gram-Schmidt basis vectors and 12 principal components required for a minimal
match of 0.9. This implies that there are many common features in the chosen
waveforms. Additionally, we observe the chosen waveforms have very similar
features and a minimal match of 0.7 can be obtained by decomposing only one
third of the entire set of waveforms in the chosen catalogue. We discuss the
implications of this observation and the advantages of eigen-decomposing
waveform catalogues with Principal Component Analysis.
