  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 functional 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.

Computer Vision and Pattern Recognition