Analog system for computing sparse codes
Abstract
A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of nonzero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually oneway) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.
 Inventors:

 (El Cerrito, CA)
 (Houston, TX)
 (San Francisco, CA)
 Issue Date:
 Research Org.:
 William Marsh Rice University (Houston, TX)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1013614
 Patent Number(s):
 7,783,459
 Application Number:
 12/035,424
 Assignee:
 William Marsh Rice University (Houston, TX) CHO
 DOE Contract Number:
 FC0201ER25462
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., and Ortman, Robert Lowell. Analog system for computing sparse codes. United States: N. p., 2010.
Web.
Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., & Ortman, Robert Lowell. Analog system for computing sparse codes. United States.
Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., and Ortman, Robert Lowell. Tue .
"Analog system for computing sparse codes". United States. https://www.osti.gov/servlets/purl/1013614.
@article{osti_1013614,
title = {Analog system for computing sparse codes},
author = {Rozell, Christopher John and Johnson, Don Herrick and Baraniuk, Richard Gordon and Olshausen, Bruno A. and Ortman, Robert Lowell},
abstractNote = {A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of nonzero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually oneway) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {8}
}
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