论文标题
MBAT矢量符号架构的正交矩阵和JSON的“软” VSA表示
Orthogonal Matrices for MBAT Vector Symbolic Architectures, and a "Soft" VSA Representation for JSON
论文作者
论文摘要
向量符号体系结构（VSA）提供了一种将复杂对象表示为单个固定长度向量的方法，以便相似的对象具有相似的向量表示。然后，这些向量表示易于用于机器学习或最近的邻居搜索。我们回顾了一种先前提出的VSA方法MBAT（添加术语的矩阵结合），该方法使用随机矩阵乘法用于与结合相关的术语。但是，乘以这样的矩阵会引入可能损害性能的不稳定性。使随机矩阵成为正交矩阵可以解决此问题。关于较大规模的应用程序，我们看到了如何为JSON中表达的任何数据应用MBAT矢量表示形式。 JSON用于多种编程语言来表达复杂的数据，但其本机格式似乎非常适合机器学习。将JSON表示为固定长度向量，因此很容易用于机器学习和最近的邻居搜索。创建这样的JSON向量还表明，VSA需要采用非交互性的绑定操作。 VSA现在准备尝试进行全面实用应用，包括医疗保健，药物和基因组学。 关键字：MBAT（添加术语的矩阵绑定），VSA（向量符号体系结构），HDC（超维度计算），分布式表示，绑定，正交矩阵，重复连接，机器学习，机器学习，搜索，JSON，JSON，JSON，VSA应用程序，VSA应用程序
Vector Symbolic Architectures (VSAs) give a way to represent a complex object as a single fixed-length vector, so that similar objects have similar vector representations. These vector representations then become easy to use for machine learning or nearest-neighbor search. We review a previously proposed VSA method, MBAT (Matrix Binding of Additive Terms), which uses multiplication by random matrices for binding related terms. However, multiplying by such matrices introduces instabilities which can harm performance. Making the random matrices be orthogonal matrices provably fixes this problem. With respect to larger scale applications, we see how to apply MBAT vector representations for any data expressed in JSON. JSON is used in numerous programming languages to express complex data, but its native format appears highly unsuited for machine learning. Expressing JSON as a fixed-length vector makes it readily usable for machine learning and nearest-neighbor search. Creating such JSON vectors also shows that a VSA needs to employ binding operations that are non-commutative. VSAs are now ready to try with full-scale practical applications, including healthcare, pharmaceuticals, and genomics. Keywords: MBAT (Matrix Binding of Additive Terms), VSA (Vector Symbolic Architecture), HDC (Hyperdimensional Computing), Distributed Representations, Binding, Orthogonal Matrices, Recurrent Connections, Machine Learning, Search, JSON, VSA Applications
