Interactive Generative Manifold Learning

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-M-0042
Agency Tracking Number: N122-138-0852
Amount: $149,903.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: N122-138
Solicitation Number: 2012.2
Small Business Information
4721 Emperor Blvd., Suite 330, Durham, NC, -
DUNS: 147201342
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Patrick Rabenold
 Engineer 4
 (919) 323-3452
 prabenold@siginnovations.com
Business Contact
 Samantha Venters
Title: CFO
Phone: (919) 323-3449
Email: sventers@siginnovations.com
Research Institution
 Stub
Abstract
Signal Innovations Group proposes a hierarchical Bayesian approach for non-linear dimensionality reduction that addresses three key challenges: learning a reversible mapping from a high-dimensional observed space to a low-dimensional embedded space, learning the dimension of the embedded space, and generating new high-dimensional data for a given location in the embedded space. The proposed generative approach is statistical and jointly learns the probabilistic reversible mapping and the dimension of the embedded space. The proposed approach also enables new high-dimensional data to be embedded in a previously learned low-dimensional space. A hierarchical Bayesian method is also proposed to learn a non-linear dynamic model in the low-dimensional space, allowing joint analysis of multiple types of dynamic data, synthesis of new dynamic data in the low-dimensional space, and mapping synthesized data to the high-dimensional observation space. The models are designed to uncover the relevant characteristics and structure of data through non-linear dimensionality reduction, which enables a human analyst to identify and explore the characteristics in the low-dimensional manifold space and generate new unobserved high-dimensional data.

* Information listed above is at the time of submission. *

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