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Faster Imputation Using Singular Value Decomposition for Sparse Data

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Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13995))

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Abstract

With the emergence of many knowledge-based systems worldwide, there have been more and more applications using different kinds of data and solving significant daily problems. Among that, the issues of missing data in such systems have become more popular, especially in data-driven areas. Other research on the imputation problem has dealt with partial and missing data. This study aims to investigate the imputation techniques for sparse data using the Singular Value Decomposition technique, namely SVDI. We explore the application of the SVDI framework for image classification and text classification tasks that involve sparse data. The experimental results show that the proposed SVDI method improves the speed and accuracy of the imputation process when compared to the PCAI method. We aim to publish our codes related to the SVDI later for the relevant research community.

Supported by Vietnam National University Ho Chi Minh City under the grant number DS2023-18-01.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews.

  2. 2.

    https://github.com/zalandoresearch/fashion-mnist.

  3. 3.

    http://yann.lecun.com/exdb/mnist/.

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Acknowledgments

This research is funded by Vietnam National University Ho Chi Minh City in Vietnam under the funding/grant number DS2023-18-01.

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Authors and Affiliations

  1. AISIA Research Lab, Ho Chi Minh City, Vietnam

    Bao H. Le & Binh T. Nguyen

  2. University of Science, Ho Chi Minh City, Vietnam

    Phuc Nguyen, Linh G. H. Tran, Bao H. Le, Thuong H. T. Nguyen & Binh T. Nguyen

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Phuc Nguyen, Linh G. H. Tran, Bao H. Le, Thuong H. T. Nguyen, Hien D. Nguyen & Binh T. Nguyen

  4. Simula Metropolitan, Oslo, Norway

    Thu Nguyen

  5. University of Information Technology, Ho Chi Minh City, Vietnam

    Hien D. Nguyen

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  1. Phuc Nguyen
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  4. Thuong H. T. Nguyen
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Correspondence to Binh T. Nguyen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University Iwate, Iwate, Japan

    Hamido Fujita

  4. Wroclaw University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Nguyen, P. et al. (2023). Faster Imputation Using Singular Value Decomposition for Sparse Data. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_11

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  • DOI: https://doi.org/10.1007/978-981-99-5834-4_11

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  • Print ISBN: 978-981-99-5833-7

  • Online ISBN: 978-981-99-5834-4

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