  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.

Machine Learning;Information Retrieval;Machine Learning