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A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14325))

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Abstract

High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning limited interactions among numerous nodes. Knowledge discovery from HDI data is a vital issue in the domain of data science due to their embedded rich patterns like node behaviors, where the fundamental task is to perform HDI data representation learning. Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a Linear Bias Incorporation (LBI) scheme is effective in preventing the model from the training overshooting and fluctuation for good convergence. However, existing LBI schemes are all statistic ones where the linear biases are fixed, which significantly restricts the scalability of the resultant NLFA model and results in loss of representation learning ability to HDI data. Motivated by the above discoveries, this paper innovatively presents a Dynamic Linear Bias Incorporation (DLBI) scheme. It firstly extends the linear bias vectors into matrices, and then builds a binary weight matrix to switch from the linear biases’ active states to their inactive states. The weight matrix’s each entry is manipulated between the binary states dynamically according to variation of the linear bias value, thereby establishing the dynamic linear biases for an NLFA model. Empirical studies on three HDI datasets from real applications indicate that the proposed DLBI-based NLFA outperforms state-of-the-art models in representation accuracy.

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Acknowledgments

This work was supported by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110579 and 2021B1515140046.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Yurong Zhong, Zhe Xie, Weiling Li & Xin Luo

Authors
  1. Yurong Zhong
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  2. Zhe Xie
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  3. Weiling Li
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  4. Xin Luo
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Corresponding author

Correspondence to Weiling Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS (Malaysia), Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhong, Y., Xie, Z., Li, W., Luo, X. (2024). A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_5

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  • DOI: https://doi.org/10.1007/978-981-99-7019-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7018-6

  • Online ISBN: 978-981-99-7019-3

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