By Topic

Sparse Decompositions for Exploratory Pattern Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hoffman, Richard L. ; Department of Computer Science, Michigan State University, East Lansing, MI 48824; Department of Electrical Engineering and Computer Science, University of Illinois at Chicago, Chicago, IL 60680. ; Jain, Anil K.

We define and verify the utility of a pattern analysis procedure called sparse decomposition. This technique involves sequentially ``peeling'' sparse subsets of patterns from a pattern set, where sparse subsets are sets of patterns which possess a certain degree of regularity or compactness as measured by a compactness measure c. If this is repeated until all patterns are deleted, then the sequence of decomposition ``layers'' derived by this procedure provides a wealth of information from which inferences about the original pattern set may be made. A statistic P is derived from this information and is shown to be powerful in detecting clustering tendency for data in reasonably compact sampling windows. The test is applied to both synthetic and real data.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-9 ,  Issue: 4 )