Hierarchical Topic Model for Tensor Data and Extraction of Weekly and Daily Patterns from Activity Monitor Records

Shunichi Nomura, Michiko Watanabe, Yuko Oguma

研究成果: Conference contribution

抄録

Latent Dirichlet allocation (LDA) is a popular topic model for extracting common patterns from discrete datasets. It is extended to the pachinko allocation model (PAM) with a hierarchical topic structure. This paper presents a combination meal allocation (CMA) model, which is a further enhanced topic model from the PAM that has both hierarchical categories and hierarchical topics. We consider count datasets in multiway arrays, i.e., tensors, and introduce a set of topics to each mode of the tensors. The topics in each mode are interpreted as patterns in the topics and categories in the next mode. Despite there being a vast number of combinations in multilevel categories, our model provides simple and interpretable patterns by sharing the topics in each mode. Latent topics and their membership are estimated using Markov chain Monte Carlo (MCMC) methods. We apply the proposed model to step-count data recorded by activity monitors to extract some common activity patterns exhibited by the users. Our model identifies four daily patterns of ambulatory activities (commuting, daytime, nighttime, and early-bird activities) as sub-topics, and six weekly activity patterns as super-topics. We also investigate how the amount of activity in each pattern dynamically affects body weight changes.

本文言語English
ホスト出版物のタイトルTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings
編集者Manish Gupta, Ganesh Ramakrishnan
出版社Springer Science and Business Media Deutschland GmbH
ページ17-30
ページ数14
ISBN(印刷版)9783030750145
DOI
出版ステータスPublished - 2021
イベントWorkshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online
継続期間: 2021 5 112021 5 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12705 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

ConferenceWorkshop on Smart and Precise Agriculture, WSPA 2021, PAKDD 2021 Workshop on Machine Learning for MEasurement Informatics, MLMEIN 2021, 1st Workshop and Shared Task on Scope Detection of the Peer Review Articles, SDPRA 2021, 1st International Workshop on Data Assessment and Readiness for AI, DARAI 2021 and 1st International Workshop on Artificial Intelligence for Enterprise Process Transformation, AI4EPT 2021 held in conjunction with 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
CityVirtual, Online
Period21/5/1121/5/14

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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