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)是源自计算神经科学的数据代表性和学习的范例。 HDC将数据作为高维、低精度的矢量,可用于各种信息处理任务,如学习或回忆。高维空间的测绘是HDC的一个基本问题,当输入数据本身是高维时,现有方法会遇到可缩放问题。在这项工作中,我们探索基于散列的串流编码技术。我们正式表明,这些方法在学习应用的性能方面享有可比的保障,同时比现有的替代方法效率要高得多。我们通过高维分类问题实验了这些结果,并表明我们的方法可以很容易地向大数据集过渡。