TY - JOUR
T1 - Adaptive Robust Low-Rank 2-D Reconstruction with Steerable Sparsity
AU - Zhang, Rui
AU - Zhang, Han
AU - Li, Xuelong
AU - Nie, Feiping
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Existing image reconstruction methods frequently improve their robustness by using various nonsquared loss functions, which are still potentially sensitive to the outliers. More specifically, when certain samples in data sets encounter severe contamination, these methods cannot identify and filter out the ill ones, and thus lead to the functional degeneration of the associated models. To address this issue, we propose a general framework, named robust and sparse weight learning (RSWL), to compute the adaptive weights based on an objective for robustness and sparsity. More importantly, the degree of the sparsity is steerable, such that only k well-reserved samples are activated during the optimization of our model. As a result, the severely polluted or damaged samples are eliminated, and the robustness is ensured. The framework is further leveraged against a 2-D image reconstruction task. Theoretical analysis and extensive experiments are presented to demonstrate the superiority of the proposed method.
AB - Existing image reconstruction methods frequently improve their robustness by using various nonsquared loss functions, which are still potentially sensitive to the outliers. More specifically, when certain samples in data sets encounter severe contamination, these methods cannot identify and filter out the ill ones, and thus lead to the functional degeneration of the associated models. To address this issue, we propose a general framework, named robust and sparse weight learning (RSWL), to compute the adaptive weights based on an objective for robustness and sparsity. More importantly, the degree of the sparsity is steerable, such that only k well-reserved samples are activated during the optimization of our model. As a result, the severely polluted or damaged samples are eliminated, and the robustness is ensured. The framework is further leveraged against a 2-D image reconstruction task. Theoretical analysis and extensive experiments are presented to demonstrate the superiority of the proposed method.
KW - Adaptive weight
KW - global robustness
KW - low-rank 2-D image reconstruction
KW - steerable sparsity
UR - http://www.scopus.com/inward/record.url?scp=85090251739&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2019.2944650
DO - 10.1109/TNNLS.2019.2944650
M3 - 文章
C2 - 31675345
AN - SCOPUS:85090251739
SN - 2162-237X
VL - 31
SP - 3754
EP - 3759
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 9
M1 - 8886728
ER -