Latent common manifold learning with alternating diffusion: Analysis and applications

Ronen Talmon, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review


The analysis of data sets arising from multiple sensors has drawn significant research attention over the years. Traditional methods, including kernel-based methods, are typically incapable of capturing nonlinear geometric structures. We introduce a latent common manifold model underlying multiple sensor observations for the purpose of multimodal data fusion. A method based on alternating diffusion is presented and analyzed; we provide theoretical analysis of the method under the latent common manifold model. To exemplify the power of the proposed framework, experimental results in several applications are reported.

Original languageEnglish (US)
Pages (from-to)848-892
Number of pages45
JournalApplied and Computational Harmonic Analysis
Issue number3
StatePublished - Nov 2019


  • Alternating diffusion
  • Common manifold
  • Data fusion
  • Diffusion maps
  • Manifold learning
  • Multimodal sensor
  • Seasonality
  • Sensor fusion

ASJC Scopus subject areas

  • Applied Mathematics


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