Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Joint Cross-view Heterogeneous Discriminative Subspace Learning via Low-rank Representation

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2296558,
        author={Yu  Ding and Ao  Li},
        title={Joint Cross-view Heterogeneous Discriminative Subspace Learning via Low-rank Representation},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={multi-view discriminative analysis cross-view subspace learning low-rank representation},
        doi={10.4108/eai.27-8-2020.2296558}
    }
    
  • Yu Ding
    Ao Li
    Year: 2020
    Joint Cross-view Heterogeneous Discriminative Subspace Learning via Low-rank Representation
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2296558
Yu Ding1, Ao Li1,*
  • 1: Harbin University of Science and Technology
*Contact email: dargonboy@126.com

Abstract

The cross-view data are very unforced to capture due to the face that different viewpoints or data collected from different sensors are already very common in recent years. However, cross-view data from different views present a significant difference, that is, cross-view data from different categories but in the same view have a higher similarity than the same category but within different views. To solve this problem, we have developed a dual low-rank representation framework to unbind these interleaved structures in a learning space. In addition, we consider that each cross-view sample of the same category is from isomorphic and heterogeneous information of two interlaced structures. Hence, we propose a powerful joint cross-view heterogeneous subspace feature learning model. In addition, the subspace learned by our algorithm contains more useful information and is more adaptable to cross-view data.