HyperSeed: Unsupervised Learning with Vector Symbolic Architectures
  Motivated by recent innovations in biologically-inspired neuromorphic
hardware, this paper presents a novel unsupervised machine learning approach
named Hyperseed that leverages Vector Symbolic Architectures (VSA) for fast
learning a topology preserving feature map of unlabelled data. It relies on two
major capabilities of VSAs: the binding operation and computing in
superposition. In this paper, we introduce the algorithmic part of Hyperseed
expressed within Fourier Holographic Reduced Representations VSA model, which
is specifically suited for implementation on spiking neuromorphic hardware. The
two distinctive novelties of the Hyperseed algorithm are: 1) Learning from only
few input data samples and 2) A learning rule based on a single vector
operation. These properties are demonstrated on synthetic datasets as well as
on illustrative benchmark use-cases, IRIS classification and a language
identification task using n-gram statistics.
