Online Interactive Generative Multiscale Manifold Learning Navy SBIR FY2012.2 Sol No.: Navy SBIR FY2012.2 Topic No.: N122-138 Topic Title: Online Interactive Generative Multiscale Manifold Learning Proposal No.: N122-138-0747 Firm: Plain Sight Systems Inc 19 Whitney Avenue New Haven, Connecticut 06510-1219 Contact: Ronald Coifman Phone: (203) 285-8617 Web Site: www.plainsight.com Abstract: One of the main obstacles to the useful exploitation of dimensionality reduction, both linear and nonlinear, is the lack of effective synthesis methods to generate examples in the original exploitation space, as opposed to the low dimensional parameter space. The ability to interactively navigate in the appropriate relevant low dimensional representation, and simultaneously observe the related "original" data modality would enable both sensor fusion and enhanced recognition and identification. Our team has developed an initial theoretical framework that promises to provide this ability, which we propose to demonstrate as Fast Online Interactive Generative Multiscale Manifold Learning. Benefits: We view our methodology as providing a systematic toolbox for natural high dimensional data exploration and analysis. Since our approach is data independent and does not require any expert knowledge or a priori labeling, the potential for applications in all areas of target and anomaly detection is vast. Commercial applications to medical and other imaging diagnostics are promising. We will pursue these DoD applications with our Navy partners and continue our work with Raytheon and Lockheed Martin to apply such methods for sensor management. Return