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.

翻译：潜变量模型（LVM）通常用于捕捉观测数据中的潜在依赖关系、模式和隐藏结构。在单通道LVM应用中，数据汉克尔化预处理步骤会产生源重复现象，这阻碍了LVM的实际应用。本文介绍了一个名为spectrally-regularised-LVMs的Python工具包。该工具包通过引入新颖的光谱正则化项来解决源重复问题。该工具包为单通道LVM应用中的光谱正则化提供了框架，从而更便于研究和使用带有光谱正则化的LVM。这通过将潜在LVM目标函数的符号或显式表示纳入框架来实现，该框架在LVM参数估计过程中采用光谱正则化。本工具包的目标是提供一个统一的光谱正则化线性LVM优化框架，并适用于单通道时间序列应用。