Learning Dictionaries as a Sum of Kronecker Products

Dantas, Cassio Fraga; da Costa, Michele N.; Lopes, Renato da Rocha

Advanced search

Search - Use quotation marks to get a more specific result

Index

Field of knowledge

Start date

Betweenand

(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Learning Dictionaries as a Sum of Kronecker Products

Full text
Author(s):	Dantas, Cassio Fraga ; da Costa, Michele N. ; Lopes, Renato da Rocha Total Authors: 3
Document type:	Journal article
Source:	IEEE SIGNAL PROCESSING LETTERS; v. 24, n. 5, p. 559-563, MAY 2017.
Web of Science Citations:	9
Abstract
The choice of an appropriate frame, or dictionary, is a crucial step in the sparse representation of a given class of signals. Traditional dictionary learning techniques generally lead to unstructured dictionaries that are costly to deploy and train, and do not scale well to higher dimensional signals. In order to overcome such limitation, we propose a learning algorithm that constrains the dictionary to be a sum of Kronecker products of smaller subdictionaries. This approach, named sum of Kronecker products, is demonstrated experimentally in an image denoising application. (AU)

FAPESP's process:	14/23936-4 - Applications of multidimensional data processing using tensor methods
Grantee:	Michele Nazareth da Costa
Support Opportunities:	Scholarships in Brazil - Post-Doctoral

Short URL