An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint
Joint Authors
Wang, Wenwu
Zhang, Ye
Yu, Tenglong
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-13
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Two common problems are often encountered in analysis dictionary learning (ADL) algorithms.
The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements.
This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm.
The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm.
Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions.
Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.
American Psychological Association (APA)
Zhang, Ye& Yu, Tenglong& Wang, Wenwu. 2014. An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051348
Modern Language Association (MLA)
Zhang, Ye…[et al.]. An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1051348
American Medical Association (AMA)
Zhang, Ye& Yu, Tenglong& Wang, Wenwu. An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051348
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051348