title: Joint manifolds for data fusion.
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.
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305 USA.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Davenport
authors.forename: Chinmay
authors.initials: C
authors.lastname: Hegde
authors.forename: Marco F
authors.initials: MF
authors.lastname: Duarte
authors.forename: Richard G
authors.initials: RG
authors.lastname: Baraniuk