Binary Random Projections with Controllable Sparsity Patterns
This work addresses the need for more efficient and accurate random projection methods in data processing tasks, offering an incremental improvement over existing approaches.
The paper tackles the problem of random projection using binary matrices with controllable sparsity patterns, proposing two sparse binary projection models that achieve significant computational advantages and improved accuracies in empirical evaluations compared to conventional dense models.
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.