  Non-Hermitian topological phases bear a number of exotic properties, such as
the non-Hermitian skin effect and the breakdown of conventional bulk-boundary
correspondence. In this paper, we introduce an unsupervised machine learning
approach to classify non-Hermitian topological phases based on diffusion maps,
which are widely used in manifold learning. We find that the non-Hermitian skin
effect will pose a notable obstacle, rendering the straightforward extension of
unsupervised learning approaches to topological phases for Hermitian systems
ineffective in clustering non-Hermitian topological phases. Through theoretical
analysis and numerical simulations of two prototypical models, we show that
this difficulty can be circumvented by choosing the on-site elements of the
projective matrix as the input data. Our results provide a valuable guidance
for future studies on learning non-Hermitian topological phases in an
unsupervised fashion, both in theory and experiment.
