Analysis Dictionary Learning based Classification: Structure for Robustness

TL;DR

This paper introduces a structured analysis dictionary learning method with a union of subspaces for improved classification robustness, including a distributed approach for large datasets, achieving high accuracy with reduced computational cost.

Contribution

It proposes a novel discriminative structured analysis dictionary learning framework with a union of subspaces and a distributed training method for large-scale visual classification.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Reduces training and testing computational complexity.

Effectively handles large-scale datasets with distributed learning.

Abstract

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.