  The H{\alpha} spectral line is a well-studied absorption line revealing
properties of the highly structured and dynamic solar chromosphere. Typical
features with distinct spectral signatures in H{\alpha} include filaments and
prominences, bright active-region plages, superpenumbrae around sunspots,
surges, flares, Ellerman bombs, filigree, and mottles and rosettes, among
others. This study is based on high-spectral resolution H{\alpha} spectra
obtained with the echelle spectrograph of the Vacuum Tower Telescope (VTT)
located at Observatorio del Teide (ODT), Tenerife, Spain. The t-distributed
Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm, which is
used for nonlinear dimensionality reduction. In this application, it projects
H{\alpha} spectra onto a two-dimensional map, where it becomes possible to
classify the spectra according to results of Cloud Model (CM) inversions. The
CM parameters optical depth, Doppler width, line-of-sight velocity, and source
function describe properties of the cloud material. Initial results of t-SNE
indicate its strong discriminatory power to separate quiet-Sun and plage
profiles from those that are suitable for CM inversions. In addition, a
detailed study of various t-SNE parameters is conducted, the impact of seeing
conditions on the classification is assessed, results for various types of
input data are compared, and the identified clusters are linked to
chromospheric features. Although t-SNE proves to be efficient in clustering
high-dimensional data, human inference is required at each step to interpret
the results. This exploratory study provides a framework and ideas on how to
tailor a classification scheme towards specific spectral data and science
questions.
