Interactive Generative Manifold Learning

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$79,982.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
N00014-13-M-0022
Award Id:
n/a
Agency Tracking Number:
N122-138-0014
Solicitation Year:
2012
Solicitation Topic Code:
N122-138
Solicitation Number:
2012.2
Small Business Information
11600 Sunrise Valley Drive, Suite # 210, Reston, VA, 20191
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator:
Ping Wang
Principal Investigator
(703) 654-9352
pwang@objectvideo.com
Business Contact:
Paul Brewer
VP, New Technology
(703) 725-3084
pbrewer@objectvideo.com
Research Institution:
Stub




Abstract
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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