State duration and interval modeling in hidden semi-Markov model for sequential data analysis

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2017-08-31 DOI:10.1007/s10472-017-9561-y
Hiromi Narimatsu, Hiroyuki Kasai
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引用次数: 11

Abstract

Sequential data modeling and analysis have become indispensable tools for analyzing sequential data, such as time-series data, because larger amounts of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input-output relations and correlation among datasets. However, because most studies in this area are specialized or limited to their respective applications, rigorous requirement analysis of such models has not been undertaken from a general perspective. Therefore, we particularly examine the structure of sequential data, and extract the necessity of “state duration” and “state interval” of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to add representational capability of a state interval of events onto HSMM. To this end, we propose two extended models: an interval state hidden semi-Markov model (IS-HSMM) to express the length of a state interval with a special state node designated as “interval state node”; and an interval length probability hidden semi-Markov model (ILP-HSMM) which represents the length of the state interval with a new probabilistic parameter “interval length probability.” Exhaustive simulations have revealed superior performance of the proposed models in comparison with HSMM. These proposed models are the first reported extensions of HMM to support state interval representation as well as state duration representation.

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序列数据分析中隐半马尔可夫模型的状态持续时间和区间建模
序列数据建模和分析已经成为分析序列数据(如时间序列数据)的不可或缺的工具,因为已经可以获得大量的感测事件数据。这些方法捕获感兴趣数据的顺序结构,例如输入输出关系和数据集之间的相关性。然而,由于该领域的大多数研究都是专门的或仅限于其各自的应用,因此尚未从一般角度对此类模型进行严格的需求分析。因此,我们特别研究了序列数据的结构,并提取了事件的“状态持续时间”和“状态间隔”的必要性,以高效、丰富地表示序列数据。特别是针对表示模型内这种状态持续时间的隐藏半马尔可夫模型(HSMM),我们试图在HSMM上添加事件状态间隔的表示能力。为此,我们提出了两个扩展模型:区间状态隐半马尔可夫模型(IS-HMM),用一个特殊的状态节点指定为“区间状态节点”来表示状态区间的长度;以及区间长度概率隐半马尔可夫模型(ILP-HSMM),该模型用新的概率参数“区间长度概率”表示状态区间的长度。详尽的仿真表明,与HSMM相比,所提出的模型具有优越的性能。这些提出的模型是HMM的首次扩展,以支持状态间隔表示和状态持续时间表示。
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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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