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Node Importance-Based Semi-supervised Nonnegative Matrix Factorization for Image Clustering

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Data Mining and Big Data (DMBD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2018))

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Abstract

As a typical dimensionality reduction method, non-negative matrix factorization (NMF) is widely used in image clustering tasks. However, the traditional NMF is unsupervised and cannot fully utilize the label information. In this research, we propose a joint feature representation and node importance propagation framework called semi-supervised nonnegative matrix factorization based on node importance (NISNMF). Firstly, by considering the overlap degree of the shared neighbors of the labeled node, a new importance index is proposed based on local structure to increase the influence of important node. Secondly, NISNMF integrates semi-supervised non-negative matrix factorization and node importance propagation into a unified feature representation and information propagation framework to improve the discriminative ability of the model. Finally, an efficient alternating iteration method is designed and its convergence is proved. Experimental results in a large number of image clustering tasks verify the superiority of the algorithm. In addition, the accuracy, parameters and sensitivity of the algorithm are also experimentally investigated.

Supported by Natural Science Basic Research Program of Shaanxi (No. 2021JM-133).

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistic, Xidian University, Xian, China

    Jintao Wu & Youlong Yang

Authors
  1. Jintao Wu
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  2. Youlong Yang
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Corresponding author

Correspondence to Youlong Yang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Techn, Shenzhen, China

    Yuhui Shi

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

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Wu, J., Yang, Y. (2024). Node Importance-Based Semi-supervised Nonnegative Matrix Factorization for Image Clustering. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2023. Communications in Computer and Information Science, vol 2018. Springer, Singapore. https://doi.org/10.1007/978-981-97-0844-4_6

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  • DOI: https://doi.org/10.1007/978-981-97-0844-4_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0843-7

  • Online ISBN: 978-981-97-0844-4

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