Hyperdimensional computing (HDC), also known as vector symbolic architectures
(VSA), is a computing framework used within artificial intelligence and
cognitive computing that operates with distributed vector representations of
large fixed dimensionality. A critical step for designing the HDC/VSA solutions
is to obtain such representations from the input data. Here, we focus on
sequences and propose their transformation to distributed representations that
both preserve the similarity of identical sequence elements at nearby positions
and are equivariant to the sequence shift. These properties are enabled by
forming representations of sequence positions using recursive binding and
superposition operations. The proposed transformation was experimentally
investigated with symbolic strings used for modeling human perception of word
similarity. The obtained results are on a par with more sophisticated
approaches from the literature. The proposed transformation was designed for
the HDC/VSA model known as Fourier Holographic Reduced Representations.
However, it can be adapted to some other HDC/VSA models.

本研究提出了一种基于超维计算和向量符号架构的模型，该模型使用分布式表示对大型固定维度的数据进行一系列操作，其中包括将序列转换为分布式表示，同时保留相同序列元素和序列位移的相似性，并采用递归绑定和叠加操作形成序列位置的表示。该模型名为傅里叶全息约减表示，但可适用于其他超维计算和向量符号架构模型。