Random projection is often used to project higher-dimensional vectors onto a lower-dimensional 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.
|Original language||English (US)|
|Journal||Journal of the Operations Research Society of China|
|State||Accepted/In press - 2022|
Bibliographical noteFunding Information:
Wen-Ye Li’s work was partially supported by Guangdong Fundamental Research Fund (No. 2021A1515011825) and Shenzhen Fundamental Research Fund (No. KQJSCX20170728162302784).
© 2022, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.
- Binary random projection