Joint Manifolds for Data Fusion

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 such a data deluge is to develop low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a low-dimensional set 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 vari...

• Type: Journal
• Year: 2010
• Where: TIP
• Authors: Mark A. Davenport, Chinmay Hegde, Marco F. Duarte, Richard G. Baraniuk
• Keywords: Education, Joint Manifold, Manifolds, Sensor Network, TIP 2010