  Multispectral and hyperspectral image analysis has experienced much
development in the last decade. The application of these methods to palimpsests
has produced significant results, enabling researchers to recover texts that
would be otherwise lost under the visible overtext, by improving the contrast
between the undertext and the overtext. In this paper we explore an extended
number of multispectral and hyperspectral image analysis methods, consisting of
supervised and unsupervised dimensionality reduction techniques, on a part of
the Syriac Galen Palimpsest dataset (www.digitalgalen.net). Of this extended
set of methods, eight methods gave good results: three were supervised methods
Generalized Discriminant Analysis (GDA), Linear Discriminant Analysis (LDA),
and Neighborhood Component Analysis (NCA); and the other five methods were
unsupervised methods (but still used in a supervised way) Gaussian Process
Latent Variable Model (GPLVM), Isomap, Landmark Isomap, Principal Component
Analysis (PCA), and Probabilistic Principal Component Analysis (PPCA). The
relative success of these methods was determined visually, using color
pictures, on the basis of whether the undertext was distinguishable from the
overtext, resulting in the following ranking of the methods: LDA, NCA, GDA,
Isomap, Landmark Isomap, PPCA, PCA, and GPLVM. These results were compared with
those obtained using the Canonical Variates Analysis (CVA) method on the same
dataset, which showed remarkably accuracy (LDA is a particular case of CVA
where the objects are classified to two classes).
