Computer Science – Learning
Scientific paper
2009-01-07
Computer Science
Learning
24 pages, 4 figures. Corrected typo on grant number in acknowledgements, page 1
Scientific paper
The emergence of low-cost sensor architectures for diverse modalities has made it possible to deploy sensor arrays that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these sensors acquire very high-dimensional data such as audio signals, images, and video. To cope with such high-dimensional data, we typically rely on low-dimensional models. Manifold models provide a particularly powerful model that captures the structure of high-dimensional data when it is 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 simple algorithms can exploit the joint manifold structure to improve their performance on standard signal processing applications. Additionally, recent results concerning dimensionality reduction for manifolds enable us to formulate a network-scalable data compression scheme that uses random projections of the sensed data. This scheme efficiently fuses the data from all sensors through the addition of such projections, regardless of the data modalities and dimensions.
Baraniuk Richard G.
Davenport Mark A.
Duarte Marco F.
Hegde Chinmay
No associations
LandOfFree
A Theoretical Analysis of Joint Manifolds does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A Theoretical Analysis of Joint Manifolds, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Theoretical Analysis of Joint Manifolds will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-720277