Binary Random Projections with Controllable Sparsity Patterns

TL;DR

This paper introduces two sparse binary projection models with controllable sparsity, offering computational efficiency and improved accuracy for random projections in data processing tasks.

Contribution

It proposes novel sparse binary projection models with adjustable sparsity patterns, enhancing efficiency and accuracy over traditional dense models.

Findings

Significant computational advantages due to sparsity

Improved empirical accuracy in data projections

Models applicable to general data vectors

Abstract

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.