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Proceedings Paper

Self-structuring data learning approach
Author(s): Igor Ternovskiy; James Graham; Daniel Carson
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Paper Abstract

In this paper, we propose a hierarchical self-structuring learning algorithm based around the general principles of the Stanovich/Evans framework and “Quest” group definition of unexpected query. One of the main goals of our algorithm is for it to be capable of patterns learning and extrapolating more complex patterns from less complex ones. This pattern learning, influenced by goals, either learned or predetermined, should be able to detect and reconcile anomalous behaviors. One example of a proposed application of this algorithm would be traffic analysis. We choose this example, because it is conceptually easy to follow. Despite the fact that we are unlikely to develop superior traffic tracking techniques using our algorithm, a traffic based scenario remains a good starting point if only do to the easy availability of data and the number of other known techniques. In any case, in this scenario, the algorithm would observe and track all vehicular traffic in a particular area. After some initial time passes, it would begin detecting and learning the traffic’s patters. Eventually the patterns would stabilize. At that point, “new” patterns could be considered anomalies, flagged, and handled accordingly. This is only one, particular application of our proposed algorithm. Ideally, we want to make it as general as possible, such that it can be applies to numerous different problems with varying types of sensory input and data types, such as IR, RF, visual, census data, meta data, etc.

Paper Details

Date Published: 22 June 2016
PDF: 7 pages
Proc. SPIE 9826, Cyber Sensing 2016, 98260E (22 June 2016); doi: 10.1117/12.2228739
Show Author Affiliations
Igor Ternovskiy, Air Force Research Lab. (United States)
James Graham, Ohio Univ. (United States)
Riverside Research (United States)
Daniel Carson, Riverside Research (United States)


Published in SPIE Proceedings Vol. 9826:
Cyber Sensing 2016
Igor V. Ternovskiy; Peter Chin, Editor(s)

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