TY - JOUR
T1 - Joint Sparse Regularization for Dictionary Learning
AU - Miao, Jianyu
AU - Cao, Heling
AU - Jin, Xiao Bo
AU - Ma, Rongrong
AU - Fei, Xuan
AU - Niu, Lingfeng
N1 - Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - As a powerful data representation framework, dictionary learning has emerged in many domains, including machine learning, signal processing, and statistics. Most existing dictionary learning methods use the ℓ0 or ℓ1 norm as regularization to promote sparsity, which neglects the redundant information in dictionary. In this paper, a class of joint sparse regularization is introduced to dictionary learning, leading to a compact dictionary. Unlike previous works which obtain sparse representations independently, we consider all representations in dictionary simultaneously. An efficient iterative solver based on ConCave-Convex Procedure (CCCP) framework and Lagrangian dual is developed to tackle the resulting model. Further, based on the dictionary learning with joint sparse regularization, we consider the multi-layer structure, which can extract the more abstract representation of data. Numerical experiments are conducted on several publicly available datasets. The experimental results demonstrate the effectiveness of joint sparse regularization for dictionary learning.
AB - As a powerful data representation framework, dictionary learning has emerged in many domains, including machine learning, signal processing, and statistics. Most existing dictionary learning methods use the ℓ0 or ℓ1 norm as regularization to promote sparsity, which neglects the redundant information in dictionary. In this paper, a class of joint sparse regularization is introduced to dictionary learning, leading to a compact dictionary. Unlike previous works which obtain sparse representations independently, we consider all representations in dictionary simultaneously. An efficient iterative solver based on ConCave-Convex Procedure (CCCP) framework and Lagrangian dual is developed to tackle the resulting model. Further, based on the dictionary learning with joint sparse regularization, we consider the multi-layer structure, which can extract the more abstract representation of data. Numerical experiments are conducted on several publicly available datasets. The experimental results demonstrate the effectiveness of joint sparse regularization for dictionary learning.
KW - Dictionary learning
KW - Joint sparse regularization
KW - Multi-layer structure
UR - http://www.scopus.com/inward/record.url?scp=85067285794&partnerID=8YFLogxK
U2 - 10.1007/s12559-019-09650-2
DO - 10.1007/s12559-019-09650-2
M3 - Article
AN - SCOPUS:85067285794
SN - 1866-9956
VL - 11
SP - 697
EP - 710
JO - Cognitive Computation
JF - Cognitive Computation
IS - 5
ER -