Vector diffusion maps and the connection Laplacian

A. Singer, H. T. Wu

Research output: Contribution to journalArticlepeer-review

132 Scopus citations

Abstract

We introduce vector diffusion maps (VDM), a new mathematical framework for organizing and analyzing massive high-dimensional data sets, images, and shapes. VDMis a mathematical and algorithmic generalization of diffusion maps and other nonlinear dimensionality reduction methods, such as LLE, ISOMAP, and Laplacian eigenmaps. While existing methods are either directly or indirectly related to the heat kernel for functions over the data, VDM is based on the heat kernel for vector fields. VDM provides tools for organizing complex data sets, embedding them in a low-dimensional space, and interpolating and regressing vector fields over the data. In particular, it equips the data with a metric, which we refer to as the vector diffusion distance. In the manifold learning setup, where the data set is distributed on a low-dimensional manifold M embedded in, we prove the relation between VDM and the connection Laplacian operator for vector fields over the manifold.

Original languageEnglish (US)
Pages (from-to)1067-1144
Number of pages78
JournalCommunications on Pure and Applied Mathematics
Volume65
Issue number8
DOIs
StatePublished - Aug 2012

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Applied Mathematics

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