Abstract
Non-negative matrix factorization and its variants have been utilized for computer vision and machine learning, however, they fail to achieve robust factorization when the dataset is corrupted by outliers and noise. In this paper, we propose a roust graph regularized non-negative matrix factorization method (RGRNMF) for image clustering. To improve the clustering effect on the image dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into the low-dimensional representation. Experimental results demonstrate that RGRNMF can achieve better clustering performances on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.
This work is supported by Foundation of Chongqing Municipal Key Laboratory of Institutions of Higher Education ([2017]3), Foundation of Chongqing Development and Reform Commission (2017[1007]), Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant Nos. KJQN201901218 and KJQN201901203), Natural Science Foundation of Chongqing (Grant No. cstc2019jcyj-bshX0101), Foundation of Chongqing Three Gorges University and National Science Foundation (NSF) grant #2011927 and DoD grant #W911NF1810475.
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Dai, X., Zhang, K., Li, J., Xiong, J., Zhang, N. (2020). Robust Graph Regularized Non-negative Matrix Factorization for Image Clustering. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_21
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