Transporting Densities Across Dimensions

05/25/2023
by   Michael Plainer, et al.
0

Even the best scientific equipment can only partially observe reality. Recorded data is often lower-dimensional, e.g., two-dimensional pictures of the three-dimensional world. Combining data from multiple experiments then results in a marginal density. This work shows how to transport such lower-dimensional marginal densities into a more informative, higher-dimensional joint space by leveraging time-delayed measurements from an observation process. This can augment the information from scientific equipment to construct a more coherent view. Classical transportation algorithms can be used when the source and target dimensions match. Our approach allows the transport of samples between spaces of different dimensions by exploiting information from the sample collection process. We reconstruct the surface of an implant from partial recordings of bacteria moving on it and construct a joint space for satellites orbiting the Earth by combining one-dimensional, time-delayed altitude measurements.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/18/2019

A geometric approach to the transport of discontinuous densities

Different observations of a relation between inputs ("sources") and outp...
research
02/20/2013

High-Dimensional Probability Estimation with Deep Density Models

One of the fundamental problems in machine learning is the estimation of...
research
06/11/2021

Continuous Herded Gibbs Sampling

Herding is a technique to sequentially generate deterministic samples fr...
research
09/22/2020

An adaptive transport framework for joint and conditional density estimation

We propose a general framework to robustly characterize joint and condit...
research
03/30/2020

An adaptive finite element approach for lifted branched transport problems

We consider so-called branched transport and variants thereof in two spa...
research
03/02/2018

Building a Telescope to Look Into High-Dimensional Image Spaces

An image pattern can be represented by a probability distribution whose ...
research
06/11/2020

Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data

Inter-modality covariation leveraged as a scientific principle can infor...

Please sign up or login with your details

Forgot password? Click here to reset