A spectral regularisation framework for latent variable models designed for single channel applications

TL;DR

This paper introduces a Python package that applies spectral regularisation to latent variable models, effectively addressing source duplication issues in single channel time-series data analysis.

Contribution

The paper presents a novel spectral regularisation framework and a Python package that enhances latent variable models for single channel applications by mitigating source duplication.

Findings

Addresses source duplication in single channel LVMs

Provides a flexible spectral regularisation framework

Facilitates improved LVM parameter estimation

Abstract

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.