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Interactive Generative Manifold Learning

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-M-0022
Agency Tracking Number: N122-138-0014
Amount: $79,982.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N122-138
Solicitation Number: 2012.2
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2012-10-22
Award End Date (Contract End Date): 2013-08-23
Small Business Information
11600 Sunrise Valley Drive Suite # 210
Reston, VA 20191
United States
DUNS: 038732173
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ping Wang
 Principal Investigator
 (703) 654-9352
Business Contact
 Paul Brewer
Title: VP, New Technology
Phone: (703) 725-3084
Research Institution

Exploratory data analysis is the foremost step in selecting appropriate statistical learning algorithms specialized to a dataset. We have proposed a generative framework for visualizing high dimensional data as low-dimensional manifold embedded in a high dimensional space. The method allows user to conveniently explore the space using fewer dimensions while still capturing the principal modes of variations of the high dimensional data. Specifically, we employ Gaussian Process Latent Variable Model (GPLVM) and Spectral Latent Variable Model (SLVM) to learn low-dimensional representations of the data. Probabilistic mappings between the embeddings and the original space facilitate efficient interpolation in the latent space as well as fast visualization of the interpolated latent points in the original space. To allow the user to span the manifold in an intuitive manner, we develop supervised and semi-supervised tools to relate the latent space to the meaningful feature space. These enable computation of principle direction for each point in the latent space to allow the user to traverse in a meaningful way. Further, we have proposed a principled approach to extrapolate the latent space by predicting the manifold structure in regions lying outside the existing domain of the data.

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

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