UMAP outperforms PCA and autoencoders for unsupervised classification of local structures in simulated and experimental colloidal systems.
At its core, UMAP assumes that the data lies on a low- dimensional Riemannian manifold embedded in a higher- dimensional space
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cond-mat.soft 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Comparing unsupervised learning methods for local structural identification in colloidal systems
UMAP outperforms PCA and autoencoders for unsupervised classification of local structures in simulated and experimental colloidal systems.