Abstract
The methods based on graph regularized non-negative matrix factorization have been extensively used in image and document clustering. However, these algorithms employed the fixed graph information and did not consider how to learn a graph automatically. For the sake of solving this problem, a kind of graph learning regularized non-negative matrix factorization (GLNMF) method is proposed in this paper. Specifically, self-representation regularized term is applied to generate weight matrix, which is updated iteratively during GLNMF optimization process. The final goal is to learn an adaptive graph and a good low dimensional representation. Furthermore, we derive the corresponding multiplicative update rules for our optimization problem. Image clustering experiments on three benchmark datasets indicate the significance of our proposed method.
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Acknowledgement
This work is supported in part by the National Natural Science Foundation of China Grant (No. 61906098, No. 61772284, No. 61701258), the National Key Research and Development Program of China (2018YFB1003702).
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Long, X., Xiong, J., Li, Y. (2020). Graph Learning Regularized Non-negative Matrix Factorization for Image Clustering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_41
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DOI: https://doi.org/10.1007/978-3-030-63823-8_41
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