Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of Vector Symbolic Architectures (VSA) for fast learning of a topology preserving feature map of unlabelled data. It relies on two major operations of VSA, binding and bundling. The algorithmic part of Hyperseed is expressed within Fourier Holographic Reduced Representations model, which is specifically suited for implementation on spiking neuromorphic hardware. The two primary contributions of the Hyperseed algorithm are, few-shot learning and a learning rule based on single vector operation. These properties are empirically evaluated on synthetic datasets as well as on illustrative benchmark use-cases, IRIS classification, and a language identification task using n-gram statistics. The results of these experiments confirm the capabilities of Hyperseed and its applications in neuromorphic hardware.
翻译:本文以最近生物激发的神经形态硬件的创新为动力,提出了一种新的不受监督的机器学习算法,名为超籽,它借鉴了病媒符号结构(VSA)的原则,用于快速学习未贴标签数据的地形保存特征图,它依赖VSA的两大操作,即捆绑和捆绑。超籽的算法部分表现在Fourier Holo-Long-Limproductions模型中,该模型特别适用于神经形态硬件的实施。超籽算法的两个主要贡献是:很少见识的学习和基于单一矢量操作的学习规则。这些特性根据合成数据集以及说明性基准使用案例、IRIS分类以及使用正克统计的语言识别任务进行了经验性评估。这些实验的结果证实了超种子及其在神经形态硬件中的应用能力。