  Hyperdimensional computing (HDC) is a paradigm for data representation and
learning originating in computational neuroscience. HDC represents data as
high-dimensional, low-precision vectors which can be used for a variety of
information processing tasks like learning or recall. The mapping to
high-dimensional space is a fundamental problem in HDC, and existing methods
encounter scalability issues when the input data itself is high-dimensional. In
this work, we explore a family of streaming encoding techniques based on
hashing. We show formally that these methods enjoy comparable guarantees on
performance for learning applications while being substantially more efficient
than existing alternatives. We validate these results experimentally on a
popular high-dimensional classification problem and show that our approach
easily scales to very large data sets.
