EncoderMap III: A Dimensionality Reduction Package for Feature Exploration in Molecular Simulations.

ABSTRACT: EncoderMap is a dimensionality reduction method that is tailored for the analysis of molecular simulation data. It relies on a neural network autoencoder architecture augmented with an additional multidimensional scaling (MDS)-like loss term. Due to this additional cost function between the high-dimensional input and the latent space, EncoderMap emerges as a method that has advantages over other dimensionality reduction methods and straightforward autoencoders alike. In particular, the low-dimensional projections created by EncoderMap are guided by the MDS-like cost function adopted from the sketch-map algorithm, which produces projections with a better correlation between high- and low-dimensional similarities and dissimilarities. EncoderMap has been successfully applied to a wide range o

SUBMITTER: Sawade K 

PROVIDER: S-EPMC12421662 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature