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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References
Bao, C., Ji, H., Quan, Y., Shen, Z.: Dictionary learning for sparse coding: algorithms and convergence analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1356–1369 (2015)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548–1560 (2010)
Ding, C.H., Li, T., Jordan, M.I.: Convex and semi-nonnegative matrix factorizations. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 45–55 (2008)
Gálvez-López, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Rob. 28(5), 1188–1197 (2012)
Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. arXiv preprint arXiv:1302.4389 (2013)
Gordon, G., Tibshirani, R.: Karush-kuhn-tucker conditions. Optimization 10(725/36), 725 (2012)
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, pp. 153–160 (2004)
Huang, S., Wang, H., Li, T., Li, T., Xu, Z.: Robust graph regularized nonnegative matrix factorization for clustering. Data Min. Knowl. Disc. 32(2), 483–503 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Jiang, B., Ding, C., Tang, J.: Graph-Laplacian PCA: closed-form solution and robustness. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3492–3498 (2013)
Kong, D., Ding, C., Huang, H.: Robust nonnegative matrix factorization using L21-norm. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 673–682. ACM (2011)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)
Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint 2, 1-norms minimization. In: Advances in Neural Information Processing Systems, pp. 1813–1821 (2010)
Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977–986. ACM (2014)
Nie, F., Yuan, J., Huang, H.: Optimal mean robust principal component analysis. In: International Conference on Machine Learning, pp. 1062–1070 (2014)
Saha, B., Pham, D.S., Phung, D., Venkatesh, S.: Sparse subspace clustering via group sparse coding. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 130–138. SIAM (2013)
Shi, J., Malik, J.: Normalized cuts and image segmentation. Departmental Papers (CIS), p. 107 (2000)
Yang, Y., Xu, D., Nie, F., Yan, S., Zhuang, Y.: Image clustering using local discriminant models and global integration. IEEE Trans. Image Process. 19(10), 2761–2773 (2010)
Ye, J., Zhao, Z., Wu, M.: Discriminative k-means for clustering. In: Advances in Neural Information Processing Systems, pp. 1649–1656 (2008)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2005)
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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