A spectral regularisation framework for latent variable models designed
  for single channel applications
Ryan Balshaw P. Stephan Heyns Daniel N. Wilke Stephan Schmidt
Latent variable models (LVMs) are commonly used to capture the underlying
dependencies, patterns, and hidden structure in observed data. Source
duplication is a by-product of the data hankelisation pre-processing step
common to single channel LVM applications, which hinders practical LVM
utilisation. In this article, a Python package titled
spectrally-regularised-LVMs is presented. The proposed package addresses the
source duplication issue via the addition of a novel spectral regularisation
term. This package provides a framework for spectral regularisation in single
channel LVM applications, thereby making it easier to investigate and utilise
LVMs with spectral regularisation. This is achieved via the use of symbolic or
explicit representations of potential LVM objective functions which are
incorporated into a framework that uses spectral regularisation during the LVM
parameter estimation process. The objective of this package is to provide a
consistent linear LVM optimisation framework which incorporates spectral
regularisation and caters to single channel time-series applications.