.png){kind=icon}
Return
A α-Divergence-Based Approach for Robust Dictionary Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2019.2922074.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a robust sequential dictionary learning (DL) algorithm is presented. The proposed algorithm is motivated from the maximum likelihood perspective on dictionary learning and its link to the minimization of the Kuliback-Leibler divergence. It is obtained by using a robust loss function in the data fidelity term of the DL objective instead of the usual quadratic loss. The proposed robust loss function is derived from the a-divergence as an alternative to the Kuliback-Leibler divergence, which leads to a quadratic loss. Compared to other robust approaches, the proposed loss has the advantage of belonging to class of redescending M-estimators, guaranteeing inference stability from large deviations from the Gaussian nominal noise model. The algorithm is obtained by solving a sequence of penalized rank-I matrix approximation problems, where the l(1)-norm is introduced as a penalty promoting sparsity and then using a block coordinate descent approach to estimate the unknowns. Performance comparison with similar robust DL algorithms on digit recognition, background removal, and gray-scale image denoising is performed highlighting efficacy of the proposed algorithm.
Keywords:
alpha-Divergence
dictionary learning
robust estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
ShareSaveShareSaveShareSavel1-K-SVD: A robust dictionary learning algorithm with simultaneous update.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
ShareSaveShareSaveAn ink-jet printed dual-CD ratiometric fluorescent paper-based sensor for the visual detection of Cu2+RSC Advances
OAAI
RSC AdvancesIF0
OAAIShareSaveShareSaveShareSave
View more