论文标题
在高维计算中从循环数据中学习的基础hyphevector的扩展
An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing
论文作者
论文摘要
高维计算（HDC）是基于高维随机空间的属性的计算框架。它对于在资源受限环境（例如嵌入式系统和物联网）中的机器学习特别有用，因为它在准确性，效率和鲁棒性之间取得了良好的平衡。将信息映射到Hyperspace（称为编码）是HDC中最重要的阶段。它的核心是基础式的，负责代表有意义信息的最小单位。在这项工作中，我们介绍了一项详细的研究，对基础hyppervector集合，这对HDC进行了实际贡献：1）我们提出了用于编码实数的级别hypervectors的改进； 2）我们引入了一种从循环数据中学习的方法，这是一种重要的信息类型，在机器学习中使用HDC从未解决过。经验结果表明，这些贡献通过循环数据导致了分类和回归的更准确模型。
Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such as embedded systems and IoT, as it achieves a good balance between accuracy, efficiency and robustness. The mapping of information to the hyperspace, named encoding, is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing the smallest units of meaningful information. In this work we present a detailed study on basis-hypervector sets, which leads to practical contributions to HDC in general: 1) we propose an improvement for level-hypervectors, used to encode real numbers; 2) we introduce a method to learn from circular data, an important type of information never before addressed in machine learning with HDC. Empirical results indicate that these contributions lead to considerably more accurate models for both classification and regression with circular data.
