通过潜在表征对齐进行深度时间序列聚类

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sangho Lee, Chihyeon Choi, Youngdoo Son
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引用次数: 0

摘要

实际上,从数据集中获取足够的标签信息具有挑战性。因此,人们研究了各种聚类方法,以便在没有标签信息的情况下对数据进行同质分组。最近,利用深度神经网络的深度聚类方法受到了广泛关注。然而,时间序列数据具有独特的特征,包括序列中观测值之间的时间关系,这可能会降低现有深度聚类方法应用于时间序列时的性能。尽管如此,有关深度聚类的研究很少涉及时间序列的特性。因此,我们提出了一种利用拓扑信息进行深度时间序列聚类的新方法,从而捕捉潜在的时间模式,生成面向聚类的表示。针对时间序列的拓扑信息,我们引入了一种基于潜空间表征特征分解的新型损失函数。通过在各种时间序列数据集上的实验,我们证明了与最先进的深度聚类方法相比,所提出的方法在实现卓越聚类性能方面的功效。据我们所知,这是第一种利用拓扑信息进行深度时间序列聚类的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep time-series clustering via latent representation alignment

In practice, obtaining sufficient label information from a dataset is challenging. Consequently, various clustering methods have been studied to homogeneously group data without label information. Recently, deep clustering approaches that utilize deep neural networks have garnered considerable attention. However, time series data possess unique characteristics, including temporal relationships between observations in a sequence, which can decrease the performance of existing deep clustering methods when applied to time series. Despite this, few studies on deep clustering have addressed the characteristics of time series. Thus, we propose a novel approach for deep time-series clustering using topological information, enabling the capture of underlying temporal patterns to generate cluster-oriented representations. We address the topological information of a time series by introducing a novel loss function based on the eigendecomposition of representations in latent space. Through experiments on various time-series datasets, we demonstrate the efficacy of the proposed method in achieving superior clustering performance compared to state-of-the-art deep clustering methods. To the best of our knowledge, this is the first approach that utilizes topological information for deep time-series clustering.

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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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