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.
翻译:超维计算(HDC)也称为矢量符号结构(VSA),是一个在人造智能和认知计算中使用的计算框架,使用分布式矢量表示大固定维度,设计HDC/VSA解决方案的关键步骤是从输入数据中获得这种表述。这里,我们侧重于顺序,并提议将其转换为分布式表述,既保持附近位置相同序列元素的相似性,又与序列变化不相容。这些属性通过利用循环约束和超定位操作形成序列位置的表达方式得以实现。提议的转换是用用于模拟人类对类似词的认知的符号字符串进行实验性调查的。所获得的结果与文献中较复杂的方法相同。提议的转换是为HDC/VSA模型设计的,称为“Fourier Holographic 减少表示法”。但是,它可以适用于其他HDC/VSA模型。