Scalable Dynamic Matrix Completion for Information Processing and Link Discovery

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-C-0206
Agency Tracking Number: N102-183-1149
Amount: $400,688.00
Phase: Phase II
Program: SBIR
Awards Year: 2012
Solicitation Year: 2010
Solicitation Topic Code: N102-183
Solicitation Number: 2010.2
Small Business Information
InfoBeyond Technology LLC
Suite 220 , 10400 Linn Station Road, Louisville, KY, -
DUNS: 877380530
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Bin Xie
 President
 (502) 742-9770
 Bin.Xie@InfoBeyonds.com
Business Contact
 Bin Xie
Title: President
Phone: (502) 742-9770
Email: Bin.Xie@InfoBeyonds.com
Research Institution
 Stub
Abstract
We investigate a problem of significant practical importance, namely, the recovery of the data matrix from a partial set of its entries that are collected in a noisy environment (i.e., a noisy partial matrix). Our proposed Near-Optimal Matrix Completion (NOMC) target to provide a leading approach that can improve the matrix completion accuracy. Two types of data matrix are considered. The first type is that the matrix is low rank or can be explicitly transferred to a low rank, i.e., a Euclidean distance matric converted from object locations. Our primarily experimental results for such a type demonstrate that NOMC recovers a low-rank matrix with only 10% samples while achieving the Frobenius error less than 10%. The second type is that the matrix is high rank, such as an arbitrary image. Our initial result shows that NOMC reconstructs the original image with high quality from the downsampled image while 50% image pixels are randomly removed. This also says that NOMC could reconstruct the image clearly even if 50% image pixels are randomly lost, removed, contaminated, or corrupted. In this work, our works encompass the theoretical analysis and the development of the NOMC software product.

* information listed above is at the time of submission.

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