RBDL: Robust block-Structured dictionary learning for block sparse representation

Abd‐Krim Seghouane

doi:10.1016/j.patrec.2023.06.009

RBDL: Robust block-Structured dictionary learning for block sparse representation

2023-08-01

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Aref Miri Rekavandi *

DOI：10.1016/j.patrec.2023.06.009

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Dictionary learning methods have been extensively used in different types of image and signal processing tasks. In a number of applications, the collected data/signal may have a multi-subspace structure and be perturbed with outliers. These motivate the use of robust and block-sparse signal representations. In this paper, a new algorithm for learning a block-structured dictionary in the presence of outliers is proposed. It is based on & alpha;-divergence and has the advantage of tolerating the presence of outliers. A block coordinate descent approach is adopted to obtain simple closed-form solutions for both the sparse coding and dictionary update stages. Finally, experimental results illustrating the superiority of the proposed method over some state-of-the-art dictionary learning methods, are provided.& COPY; 2023 Published by Elsevier B.V.

Keywords:

Dictionary learning

Block sparse representation

Robust

Low-rank

RBDL: Robust block-Structured dictionary learning for block sparse representation

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