Abstract
Image clustering has become one of the most popular themes in web based recommendation system. In this study, we propose a novel image clustering algorithm referred as graph regularized robust principal component analysis (GRPCA). Unlike existing spectral rotation or k-means method, no discretization step is required in our proposed method by imposing nonnegative constraint explicitly. Besides, in GRPCA an affinity graph is constructed to encode the locality manifold information, and the global graph structure is respected by applying matrix factorization. The proposed method is robust to model selection that is more appealing for real unsupervised applications. Extensive experiments on three publicly available image datasets demonstrate the effectiveness of our algorithm.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC) (81670090) and the Scientific Research Program of New Century Excellent Talents in Fujian Province University, China and Fujian Provincial Natural Science Foundation of China (Grant 2018J01570).
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Jiang, Y., Liang, W., Tang, M., Xie, Y., Tang, J. (2020). Image Clustering Based on Graph Regularized Robust Principal Component Analysis. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_45
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DOI: https://doi.org/10.1007/978-981-15-2777-7_45
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