Neural FIM for learning Fisher information metrics from point cloud data
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
Abstract
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells).
BibTeX
@inproceedings{icml2023_neuralfimforlear,
title = {Neural FIM for learning Fisher information metrics from point cloud data},
author = {Oluwadamilola Fasina and Guillaume Huguet and Alexander Tong and Yanlei Zhang and Guy Wolf and Maximilian Nickel and Ian Adelstein and Smita Krishnaswamy},
booktitle = {ICML 2023},
year = {2023}
}