AI 中文总结
本研究提出基于Nyström方法的NysHD方法，将半正定相似函数引入超维计算（HDC），在图和字符串数据集上分类准确率平均提升11%和17%，扩展了HDC可解决的问题类型。
AI 中文摘要
超维计算（HDC）是认知科学领域提出的一种方法，用于解决信息处理任务，其中数据以高维随机向量的形式表示。该技术有严谨的数学支撑，易于在FPGA、“存内计算”架构等高能效、高度并行的硬件中实现。HDC在机器学习中的有效性很大程度上取决于如何将原始数据映射到高维空间。在本研究中，我们提出NysHD，一种基于核近似文献中的Nyström方法来构建该映射的新方法。我们的方法提供了一种简单的方案，可将任何用户定义的半正定相似函数转化为HDC中的等效映射。已有大量关于此类学习问题函数设计的文献，我们的方法提供了将这些函数引入HDC场景的机制，扩展了HDC可解决的问题类型。与现有HDC编码方法的实证评估显示，NysHD在图数据集和字符串数据集上的分类准确率平均分别提升11%和17%。
英文摘要
Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vectors. The technique has a rigorous mathematical backing, and is easy to implement in energy-efficient and highly parallel hardware like FPGAs and "processing-in-memory" architectures. The effectiveness of HDC in machine learning largely depends on how raw data is mapped to high-dimensional space. In this work, we propose NysHD, a new method for constructing this mapping that is based on the Nyström method from the literature on kernel approximation. Our approach provides a simple recipe to turn any user-defined positive-semidefinite similarity function into an equivalent mapping in HDC. There is a vast literature on the design of such functions for learning problems. Our approach provides a mechanism to import them into the HDC setting, expanding the types of problems that can be tackled using HDC. Empirical evaluation against existing HDC encoding methods shows that NysHD can achieve, on average, 11% and 17% better classification accuracy on graph and string datasets respectively.
