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

Liu, Xiaofeng; Li, Zhaofeng; Kong, Lingsheng; Diao, Zhihui; Yan, Junliang; Zou, Yang; Yang, Chao; Jia, Ping; You, Jane (2018-11-29)
 
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https://doi.org/10.1109/ICPR.2018.8545267

Liu, Xiaofeng
Li, Zhaofeng
Kong, Lingsheng
Diao, Zhihui
Yan, Junliang
Zou, Yang
Yang, Chao
Jia, Ping
You, Jane
Institute of Electrical and Electronics Engineers
29.11.2018

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

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doi:https://doi.org/10.1109/ICPR.2018.8545267
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https://urn.fi/URN:NBN:fi-fe2019062722214
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Abstract

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.

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