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 non-zero 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 one-way) 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):
- 7783459
- Application Number:
- 12/035,424
- Assignee:
- William Marsh Rice University (Houston, TX)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06G - ANALOGUE COMPUTERS
- DOE Contract Number:
- FC02-01ER25462
- 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 non-zero 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 one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 24 00:00:00 EDT 2010},
month = {Tue Aug 24 00:00:00 EDT 2010}
}
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