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Latent Pattern Identification Using Orthogonal-Constraint Coupled Nonnegative Matrix Factorization

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

The recent advancements and developments in Intelligent Transportation Systems (ITS) lead to the generation of abundant spatio-temporal traffic data. Identifying or understanding the latent patterns present in these spatio-temporal traffic data is very much essential and also challenging due to the fact that there is a chance of obtaining duplicate or similar patterns during the process of common pattern identification. This paper proposes an Orthogonal-Constraint Coupled Nonnegative Matrix Factorization (OC-CNMF) method and studies how to effectively identify the common as well as distinctive patterns that are hidden in the spatio-temporal traffic-related datasets. The distinctiveness of the patterns is achieved by the imposition of the orthogonality constraint in CNMF during the process of factorization. The imposition of the orthogonal constraint helps to ignore similar/duplicate patterns among the identification of the common patterns. We have shown that imposing orthogonality constraint in CNMF improves the convergence performance of the model and is able to identify common as well as distinctive patterns. Also, the performance of the OC-CNMF model is evaluated by comparing it with various performance evaluation measures.

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Acknowledgement

This research was supported by the National Research Foundation of Korea (Grant No. 2020R1A2C1012196).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea

    Anandkumar Balasubramaniam & Anand Paul

  2. School of Computer Science and Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia

    Thirunavukarasu Balasubramaniam & Richi Nayak

Authors
  1. Anandkumar Balasubramaniam
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  2. Thirunavukarasu Balasubramaniam
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  3. Anand Paul
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  4. Richi Nayak
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Corresponding author

Correspondence to Anand Paul .

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Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Balasubramaniam, A., Balasubramaniam, T., Paul, A., Nayak, R. (2022). Latent Pattern Identification Using Orthogonal-Constraint Coupled Nonnegative Matrix Factorization. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_47

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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