Analysis Dictionary Learning Based Classification: Structure for Robustness
Journal article, 2019

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated function to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large-scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.


Wen Tang

North Carolina State University

Ashkan Panahi

North Carolina State University

Hamid Krim

North Carolina State University

Liyi Dai


IEEE Transactions on Image Processing

1057-7149 (ISSN) 19410042 (eISSN)

Vol. 28 12 6035-6046

Subject Categories

Control Engineering

Signal Processing

Computer Science



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