Data fusion and multicue data matching by diffusion maps

Stéphane Lafon, Yosi Keller, Ronald R. Coifman

Research output: Contribution to journalArticlepeer-review

275 Scopus citations

Abstract

Data fusion and multicue data matching are fundamental tasks of high-dimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is three-fold: First, we present the Laplace-Beltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a refinement of the Nystrom extension algorithm called "geometric harmonics." We also explain how to use this tool for data assimilation. Finally, we introduce a multicue data matching scheme based on nonlinear spectral graphs alignment. The effectiveness of the presented schemes is validated by applying it to the problems of lipreading and image sequence alignment.

Original languageEnglish
Pages (from-to)1784-1797
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume28
Issue number11
DOIs
StatePublished - 2006
Externally publishedYes

Keywords

  • Data mining
  • Graph algorithms
  • Graph theory
  • Image databases
  • Machine learning
  • Markov processes
  • Pattern matching

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