A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification

Various representation-based methods have been developed and shown great potential for pattern classification. To further improve their discriminability, we propose a Bi-level optimization framework in terms of both low-dimensional projection and collaborative representation. Specifically, during the projection phase, we try to minimize the intra-class similarity and inter-class dissimilarity, while in the representation phase, our goal is to achieve the lowest correlation of the representation results. Solving this joint optimization mutually reinforces both aspects of feature projection and representation. Experiments on face recognition, object categorization and scene classification dataset demonstrate remarkable performance improvements led by the proposed framework.

Authors:
Liu Xiaofeng, Li Zhaofeng, Kong Lingsheng, Diao Zhihui, Yan Junliang, Zou Yang, Yang Chao, Jia Ping, You Jane

Publication type:
A4 Article in conference proceedings

Place of publication:
2018 24th International Conference on Pattern Recognition (ICPR)

Keywords:
6G Publication

Published:

Full citation:
X. Liu et al., “A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification,” 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, 2018, pp. 1493-1498. doi: 10.1109/ICPR.2018.8545267

DOI:
https://doi.org/10.1109/ICPR.2018.8545267

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2019062722214