Joint manifolds for data fusion

Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of cameras can generate massive amounts of high-dimensional image and video data. One way to cope with this data deluge is to exploit low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a small number of parameters, as is often the case in a sensor network. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for a variety of signal processing algorithms for applications including classification and manifold learning. Additionally, recent results concerning random projections of manifolds enable us to formulate a scalable and universal dimensionality reduction scheme that efficiently fuses the data from all sensors.

Original languageEnglish (US)
Pages (from-to)2580-2594
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - Oct 2010


  • Camera networks
  • Classification
  • Data fusion
  • Manifold learning
  • Random projections
  • Sensor networks

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software


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