Ontology highlight
ABSTRACT:
SUBMITTER: Peterfreund E
PROVIDER: S-EPMC7733838 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Peterfreund Erez E Lindenbaum Ofir O Dietrich Felix F Bertalan Tom T Gavish Matan M Kevrekidis Ioannis G IG Coifman Ronald R RR
Proceedings of the National Academy of Sciences of the United States of America 20201123 49
We propose a local conformal autoencoder (LOCA) for standardized data coordinates. LOCA is a deep learning-based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from an unknown, nonlinear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized, latent variables. We assume a repeated measurement sampling strategy, common in scientific measurements, and present a method for learning an emb ...[more]