Title :
Dictionary Learning
Author :
Ivana Tosic;Pascal Frossard
Author_Institution :
She is currently a postdoctoral researcher at the Redwood Center for Theoretical Neuroscience, University of California at Berkeley, United States, where she works on the intersection of image processing and computational neuroscience domains.
Abstract :
We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.
Keywords :
"Dictionaries","Approximation methods","Encoding","Learning systems","Signal processing algorithms","Approximation algorithms","Sensors"
Journal_Title :
IEEE Signal Processing Magazine
DOI :
10.1109/MSP.2010.939537
