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Open Access

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen 518055, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with University of Chinese Academy of Sciences, Beijing 100049, China
School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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Abstract

Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm. This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation, and adopts a feature normalizing strategy to align the different views. Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework: Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization (GDCMVNMF) and Extended Multi-View Constrained Nonnegative Matrix Factorization (ExMVCNMF). The intrinsic connection between these two specific implementations is discussed, and the optimization based on multiply update rules is presented. Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

Keywords

multi-viewsemi-supervised clusteringdiscriminative informationgeometric informationfeature normalizing strategy

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Big Data Mining and Analytics
Volume 7 Issue 1,
March 2024
Pages 55-74
DOI: 10.26599/BDMA.2023.9020004
Cite this article:
Cui G, Li Y, Li J, et al. Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization. Big Data Mining and Analytics, 2024, 7(1): 55-74. https://doi.org/10.26599/BDMA.2023.9020004

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Received: 24 November 2022
Revised: 06 March 2023
Accepted: 04 April 2023
Published: 25 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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