  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
