Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives. We validate these results experimentally on a popular high-dimensional classification problem and show that our approach easily scales to very large data sets.

翻译：超维计算（HDC）是一种源自计算神经科学的数据表示与学习范式。HDC将数据表示为高维、低精度的向量，可用于学习或联想回忆等多种信息处理任务。高维空间映射是HDC中的基本问题，现有方法在处理输入数据本身具有高维特性时面临可扩展性挑战。本研究探索了一类基于哈希的流式编码技术。我们从理论上证明，这些方法在学习应用中具有与现有方案相当的性能保证，同时效率显著提升。通过在典型高维分类问题上的实验验证，结果表明我们的方法可轻松扩展至超大规模数据集。