论文标题
具有可控稀疏模式的二进制随机预测
Binary Random Projections with Controllable Sparsity Patterns
论文作者
论文摘要
随机投影通常用于将高维矢量投射到较低维空间上，同时大约保留其成对距离。它已成为各种数据处理任务的强大工具，并引起了相当大的研究兴趣。部分是由神经科学最近发现的动机，在本文中，我们研究了使用具有可控稀疏模式的二元矩阵随机投影的问题。具体而言，我们提出了两个在一般数据向量上使用的稀疏二进制投影模型。与具有密集投影矩阵的常规随机投影模型相比，由于其稀疏结构，我们提出的模型具有显着的计算优势，并改善了经验评估的准确性。
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.
