Following the general theoretical framework of VSA (Vector Symbolic
Architecture), a cognitive model with the use of sparse binary hypervectors is
proposed. In addition, learning algorithms are introduced to bootstrap the
model from incoming data stream, with much improved transparency and
efficiency. Mimicking human cognitive process, the training can be performed
online while inference is in session. Word-level embedding is re-visited with
such hypervectors, and further applications in the field of NLP (Natural
Language Processing) are explored.

基于 VSA 理论框架，提出了一种使用稀疏二进制超向量的认知模型，并引入学习算法来从输入数据流中引导模型的训练，在有效性和透明度方面有了显著改进。该模型模拟人类认知过程，在会话中可以进行在线训练，并探索了在自然语言处理领域中利用这些超向量进行词级嵌入的进一步应用。

稀疏二进制超向量的认知建模与学习

Cognitive modeling and learning with sparse binary hypervectors

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

本文介绍了一种基于 VSA 的 MBAT 方法 - 通过随机矩阵乘法实现相关术语的绑定，但这样做引入了不稳定性问题。本文提出使用正交矩阵代替随机矩阵来解决这个问题，并探讨了如何使用 MBAT 向量表示来完成 JSON 数据的机器学习和最近邻搜索。