Spectrally regularised LVMs: A spectral regularisation framework for latent variable models designed for single-channel applications

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-05-22 DOI:10.1016/j.softx.2025.102187

Ryan Balshaw , P. Stephan Heyns , Daniel N. Wilke , Stephan Schmidt

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引用次数: 0

Abstract

Latent variable models (LVMs) are commonly used to capture the underlying dependencies, patterns, and hidden structures 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 by adding 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 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. This package aims to provide a consistent linear LVM optimisation framework incorporating spectral regularisation and caters to single-channel time-series applications.

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频谱正则化lvm：为单通道应用设计的潜在变量模型的频谱正则化框架

潜变量模型（lvm）通常用于捕获观察数据中的底层依赖关系、模式和隐藏结构。源复制是单通道LVM应用程序中常见的数据汉化预处理步骤的副产品，它阻碍了LVM的实际利用。在本文中，介绍了一个名为频谱正则化lvms的Python包。提出的包通过增加一个新的频谱正则化项来解决源重复问题。这个包为单通道LVM应用程序中的频谱正则化提供了一个框架，从而使其更容易调查和利用具有频谱正则化的LVM。这是通过潜在LVM目标函数的符号或显式表示来实现的，这些目标函数被合并到一个框架中，在LVM参数估计过程中使用频谱正则化。该包旨在提供一个一致的线性LVM优化框架，结合频谱正则化，并迎合单通道时间序列应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.