  We map the surface of Pluto using an unsupervised machine learning technique
using the near-infrared observations of the LEISA/Ralph instrument onboard
NASA's New Horizons spacecraft. The principal component reduced Gaussian
mixture model was implemented to investigate the geographic distribution of the
surface units across the dwarf planet. We also present the likelihood of each
surface unit at the image pixel level. Average I/F spectra of each unit were
analyzed -- in terms of the position and strengths of absorption bands of
abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile
H${}_{2}$O -- to connect the unit to surface composition, geology, and
geographic location. The distribution of surface units shows a latitudinal
pattern with distinct surface compositions of volatiles -- consistent with the
existing literature. However, previous mapping efforts were based primarily on
compositional analysis using spectral indices (indicators) or implementation of
complex radiative transfer models, which need (prior) expert knowledge, label
data, or optical constants of representative endmembers. We prove that an
application of unsupervised learning in this instance renders a satisfactory
result in mapping the spatial distribution of ice compositions without any
prior information or label data. Thus, such an application is specifically
advantageous for a planetary surface mapping when label data are poorly
constrained or completely unknown, because an understanding of surface material
distribution is vital for volatile transport modeling at the planetary scale.
We emphasize that the unsupervised learning used in this study has wide
applicability and can be expanded to other planetary bodies of the Solar System
for mapping surface material distribution.
