We present the results of the application of locally linear embedding (LLE)
to reduce the dimensionality of dereddened and continuum subtracted
near-infrared spectra using a combination of models and real spectra of massive
protostars selected from the Red MSX Source survey database. A brief comparison
is also made with two other dimension reduction techniques; Principal Component
Analysis (PCA) and Isomap using the same set of spectra as well as a more
advanced form of LLE, Hessian locally linear embedding. We find that whilst LLE
certainly has its limitations, it significantly outperforms both PCA and Isomap
in classification of spectra based on the presence/absence of emission lines
and provides a valuable tool for classification and analysis of large spectral
data sets.Comment: 8 pages, 7 figures. Accepted for publication in MNRAS 2016 June 2
