  Fast radio bursts (FRBs) are mysterious astronomical phenomena, and it is
still uncertain whether they consist of multiple types. In this study we use
two nonlinear dimensionality reduction algorithms - Uniform Manifold
Approximation and Projection (UMAP) and t-distributed stochastic neighbour
embedding (t-SNE) - to differentiate repeaters from apparently non-repeaters in
FRBs. Based on the first Canadian Hydrogen Intensity Mapping Experiment (CHIME)
FRB catalogue, these two methods are applied to standardized parameter data and
image data from a sample of 594 sub-bursts and 535 FRBs, respectively. Both
methods are able to differentiate repeaters from apparently non-repeaters. The
UMAP algorithm using image data produces more accurate results and is a more
model-independent method. Our result shows that in general repeater clusters
tend to be narrowband, which implies a difference in burst morphology between
repeaters and apparently non-repeaters. We also compared our UMAP predictions
with the CHIME/FRB discovery of 6 new repeaters, the performance was generally
good except for one outlier. Finally, we highlight the need for a larger and
more complete sample of FRBs.
