Analysis Dictionary Learning Based Classification: Structure for Robustness
Artikel i vetenskaplig tidskrift, 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

Chalmers, Data- och informationsteknik, Data Science

Hamid Krim

North Carolina State University

Liyi Dai


IEEE Transactions on Image Processing

1057-7149 (ISSN)

Vol. 28 12 6035-6046




Datavetenskap (datalogi)



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