Journal of the Operations Research Society of China ›› 2022, Vol. 10 ›› Issue (3): 507-528.doi: 10.1007/s40305-021-00387-0

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  • 收稿日期:2021-07-12 修回日期:2021-10-15 出版日期:2022-09-30 发布日期:2022-09-06

Binary Random Projections with Controllable Sparsity Patterns

Wen-Ye Li1,2, Shu-Zhong Zhang3   

  1. 1. The Chinese University of Hong Kong, Shenzhen 518172, Guangdong, China;
    2. Shenzhen Research Institute of Big Data, Shenzhen 518052, Guangdong, China;
    3. University of Minnesota, Minneapolis, MN 55455, USA
  • Received:2021-07-12 Revised:2021-10-15 Online:2022-09-30 Published:2022-09-06
  • Contact: Wen-Ye Li,Shu-Zhong Zhang E-mail:wyli@cuhk.edu.cn;zhangs@umn.edu
  • Supported by:
    Wen-Ye Li's work was partially supported by Guangdong Fundamental Research Fund (No.2021A1515011825) and Shenzhen Fundamental Research Fund (No.KQJSCX20170728162302784).

Abstract: Random projection is often used to project higher-dimensional vectors onto a lowerdimensional space,while approximately preserving their pairwise distances.It has emerged as a powerful tool in various data processing tasks and has attracted considerable research interest.Partly motivated by the recent discoveries in neuroscience,in this paper we study the problem of random projection using binary matrices with controllable sparsity patterns.Specifically,we proposed two sparse binary projection models that work on general data vectors.Compared with the conventional random projection models with dense projection matrices,our proposed models enjoy significant computational advantages due to their sparsity structure,as well as improved accuracies in empirical evaluations.

Key words: Binary random projection, Sparsity, Dimensionality

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