TY - GEN
T1 - Hierarchical Topic Model for Tensor Data and Extraction of Weekly and Daily Patterns from Activity Monitor Records
AU - Nomura, Shunichi
AU - Watanabe, Michiko
AU - Oguma, Yuko
N1 - Funding Information:
This study was conducted as part of the “Research and Development on Utilization of Fundamental Technologies for Social Big Data” (178A04) project of NICT (National Institute of Information and Communication Technology).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Activity monitor records
KW - Tensor data
KW - Topic model
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U2 - 10.1007/978-3-030-75015-2_3
DO - 10.1007/978-3-030-75015-2_3
M3 - Conference contribution
AN - SCOPUS:85106422700
SN - 9783030750145
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 30
BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2021 Workshops, WSPA, MLMEIN, SDPRA, DARAI, and AI4EPT, 2021 Proceedings
A2 - Gupta, Manish
A2 - Ramakrishnan, Ganesh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 11 May 2021 through 14 May 2021
ER -