Joint manifolds for data fusion

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

    Research output: Contribution to journalArticle

    Abstract

    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
    Volume19
    Issue number10
    DOIs
    StatePublished - Oct 2010

    Keywords

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

    ASJC Scopus subject areas

    • Software
    • Computer Graphics and Computer-Aided Design

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  • Cite this

    Davenport, M. A., Hegde, C., Duarte, M. F., & Baraniuk, R. G. (2010). Joint manifolds for data fusion. IEEE Transactions on Image Processing, 19(10), 2580-2594. https://doi.org/10.1109/TIP.2010.2052821