2020/06/29 by Wenye Li, Li, Wenye, Shuzhong Zhang +1 · 1 voice · 1 citation
Computer Science · Engineering · Mathematics · #68W20 #Blind Source Separation Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Sparse and Compressive Sensing Techniques #cs.IR #cs.LG #msc:68W20 #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.16180
19 pages, 15 figures
arxiv created 2020/06/29 · openalex publication_date 2020/06/29 · arxiv published 2020/06/29 · arxiv updated 2020/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.