TY - GEN
T1 - High-dimensional data fusion via joint manifold learning
AU - Davenport, Mark A.
AU - Hegde, Chinmay
AU - Duarte, Marco F.
AU - Baraniuk, Richard G.
PY - 2010
Y1 - 2010
N2 - The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of data governed by a low-dimensional set of parameters.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 manifold learning. Additionally, we leverage recent results concerning random projections of manifolds to formulate a universal, network-scalable dimensionality reduction scheme that efficiently fuses the data from all sensors.
AB - The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of data governed by a low-dimensional set of parameters.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 manifold learning. Additionally, we leverage recent results concerning random projections of manifolds to formulate a universal, network-scalable dimensionality reduction scheme that efficiently fuses the data from all sensors.
UR - http://www.scopus.com/inward/record.url?scp=79960112271&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:79960112271
SN - 9781577354888
T3 - AAAI Fall Symposium - Technical Report
SP - 20
EP - 27
BT - Manifold Learning and Its Applications - Papers from the AAAI Fall Symposium, Technical Report
T2 - 2010 AAAI Fall Symposium
Y2 - 11 November 2010 through 13 November 2010
ER -