利用受污染 vMF 分布的隐马尔可夫模型研究文本数据中的制度变化

IF 2.8 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Data Mining and Knowledge Discovery Pub Date : 2024-08-03 DOI:10.1007/s10618-024-01051-w

Yingying Zhang, Shuchismita Sarkar, Yuanyuan Chen, Xuwen Zhu

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引用次数: 0

摘要

本文提出了一种分析具有散点和重尾的时间方向性数据的新方法。本文建立了一个具有受污染的 von Mises-Fisher 发射分布的隐马尔可夫模型。该模型采用前向和后向选择方法实现，为污染和非污染数据提供了额外的灵活性。在几个实验设置和两个分别包含总统讲话和公司财务报表的真实文本数据集上，演示了该方法在寻找同质时间块（制度）方面的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

On regime changes in text data using hidden Markov model of contaminated vMF distribution

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On regime changes in text data using hidden Markov model of contaminated vMF distribution

This paper presents a novel methodology for analyzing temporal directional data with scatter and heavy tails. A hidden Markov model with contaminated von Mises-Fisher emission distribution is developed. The model is implemented using forward and backward selection approach that provides additional flexibility for contaminated as well as non-contaminated data. The utility of the method for finding homogeneous time blocks (regimes) is demonstrated on several experimental settings and two real-life text data sets containing presidential addresses and corporate financial statements respectively.

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来源期刊

Data Mining and Knowledge Discovery 工程技术-计算机：人工智能

CiteScore

10.40

自引率

4.20%

发文量

审稿时长

10 months

期刊介绍： Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.