Local conformal autoencoder for standardized data coordinates

Abstract

A fundamental issue in empirical science is the ability to calibrate between different types of measurements/observations of the same phenomenon. This naturally suggests the selection of canonical variables, in the spirit of principal components, to enable matching/calibration among different observation modalities/instruments. We develop a method for extracting standardized, nonlinear, intrinsic coordinates from measured data, leading to a generalized isometric embedding of the observations. This is achieved through a local burst data acquisition strategy that allows us to capture the local z-scored structure. We implement this method using a local conformal autoencoder architecture and illustrate it computationally. The proposed embedding is fast, parallelizable, easy to implement using existing open-source neural network implementations and exhibits surprising interpolation and extrapolation capabilities.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 23, 2020
Source ID
10.1073/pnas.2014627117

Entities

People

  • Erez Peterfreund
  • Felix Dietrich
  • Matan Gavish
  • Ofir Lindenbaum
  • Ronald R. Coifman
  • Tom Bertalan
  • Yannís G. Kevrekidis

Organizations

  • Hebrew University of Jerusalem
  • Israel Science Foundation
  • Johns Hopkins University
  • Technical University of Munich
  • Yale University

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms