Abstract
In this paper, a novel method called Generalized Graph Regularized Non-Negative Matrix Factorization (GGNMF) for data representation is proposed. GGNMF is a part-based data representation which incorporates generalized geometrically-based regularizer. New updating rules are adopted for this method, and the new method convergence is proved under some specific conditions. In our experiments, we evaluated the performance of GGNMF on image clustering problems. The results show that, with the guarantee of the convergence, the proposed updating rules can achieve even better performance.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (11101420, 10831006). In addition, I am truly grateful for the inspiration and help of Deng Cai. Any opinions, findings, and conclusions expressed here are those of the authors’ and do not necessarily reflect the views of the funding agencies.
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Hao, Y., Han, C., Shao, G., Guo, T. (2013). Generalized Graph Regularized Non-negative Matrix Factorization for Data Representation. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34528-9_1
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DOI: https://doi.org/10.1007/978-3-642-34528-9_1
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