Abstract
Clustering as one of the main research methods in data mining, with the generation of multi-view data, multi-view clustering has become the research hotspot at present. Many excellent multi-view clustering algorithms have been proposed to solve various practical problems. These algorithms mainly achieve multi-view feature fusion by maximizing the consistency between views. However, in practical applications, multi-view data’ initial feature is often imbalanced, resulting in poor performance of existing multi-view clustering algorithms. Additionally, imbalanced multi-view data exhibits significant differences in feature across different views, which better reflects the complementarity of multi-view data. Therefore, it is important to fully extract feature from different views of imbalanced multi-view data. This paper proposes an imbalanced multi-view clustering algorithm based on common specific feature learning, ImMC-CSFL. Two deep networks are used to extract common and specific feature on each view, the GAN network is introduced to maximize the extraction of common feature from multi-view data, and orthogonal constraints are used to maximize the extraction of specific feature from different views. Finally, the learned imbalanced multi-view feature is input for clustering. The experiment result on three different multi-view datasets UCI Digits, BDGP, and CCV showed that our proposed algorithm had better clustering performance, and the effectiveness and robustness were verified through experiment analysis of different modules.
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References
Huang, Z., Zhou, J., Peng, X., et al.: Multi-view Spectral Clustering Network. IJCAI, pp. 2563–2569 (2019)
Zhu, X., Zhang, S., He, W., et al.: One-step multi-view spectral clustering. IEEE Trans. Knowl. Data Eng.Knowl. Data Eng. 31(10), 2022–2034 (2019)
Yin, H., Hu, W., Li, F., et al.: One-step multi-view spectral clustering by learning common and specific nonnegative embeddings. Int. J. Mach. Learn. Cybern.Cybern. 12(7), 2121–2134 (2021)
Jia, Y., Liu, H., Hou, J., et al.: Multi-view spectral clustering tailored tensor low-rank representation. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4784–4797 (2021)
El Hajjar, S., Dornaika, F., Abdallahde, F., et al.: Multi-view spectral clustering via constrained nonnegative embedding. Inf. Fusion 78, 209–217 (2022)
Gao, H., Nie, mF., Li, X., et al.: Multi-view subspace clustering. In: ICCV 2015, pp. 4238–4246 (2015)
Brbic, M., Kopriva, I.: Multi-view low-rank sparse subspace clustering. Pattern Recognit. 73, 247–258 (2018)
Li, R., Zhang, C., Hu, Q., et al.: Flexible multi-view representation learning for subspace clustering. IJCAI 2019, pp. 2916–2922 (2019)
Zhang, C., Hu, Q., Fu, H., et al.: Generalized latent multi-view subspace clustering. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 42(1), 86–99 (2020)
Kang, Z., Zhou, W., Zhao, Z., et al.: Large-scale multi-view subspace clustering in linear time. In: AAAI 2020, pp. 4412–4419 (2020)
Liu, J., Wang, C., Gao, J., et al.: Multi-view clustering via joint nonnegative matrix factorization. In: SDM 2013, 252–260 (2013)
Zhang, Y., Kong, X.W., Wang, Z.F., et al.: Cluster analysis based on multi-view matrix decomposition. J. Autom. 2018(44).12, 2160–2169 (2018)
Mekthanavanh, V., Li, T., Meng, H., et al.: Social web video clustering based on multi-view clustering via nonnegative matrix factorization. Int. J. Mach. Learn. Cybern.Cybern. 10(10), 2779–2790 (2019)
Nie, F., Shi, S., Li, X.: Auto-weighted multi-view co-clustering via fast matrix factorization. Pattern Recogn.Recogn. 102, 107207 (2020)
Liu, S.S., Lin, L.: Integrative clustering of multi-view data by nonnegative matrix factorization. ArXiv, abs/2110.13240 (2021)
Liu, X., Dou, Y., Yin, J., et al.: Multiple kernel k-means clustering with matrix-induced regularization. In: AAAI 2016, pp. 1888–1894
Zhou, S., Liu, X., Li, M., et al.: Multiple kernel clustering with neighbor-kernel subspace segmentation. IEEE Trans. Neural Networks Learn. Syst. 31(4), 1351–1362 (2020)
Liu, X., Wang, L., Zhu, X., et al.: Absent Multiple Kernel Learning Algorithms. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 42(6), 1303–1316 (2020)
Zhang, X., Ren, Z., Sun, H., et al.: Multiple kernel low-rank representation-based robust multi-view subspace clustering. Inf. Sci. 551, 324–340 (2021)
Li, Z., Wang, Q., Tao, Z., Gao, Q., Yang, Z.: Deep adversarial multi-view clustering network. In: Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China, pp. 2952–2958 (2019)
Rai, N., Negi, S., Chaudhury, S., Deshmukh, O.: Partial multi-view clustering using graph regularized NMF. In: 23rd International Conference on Pattern Recognition, Cancún, Mexico, pp. 2192–2197 (2016)
Cai, X., Wang, H., Huang, H., Ding, C.H.Q.: Joint stage recognition and anatomical annotation of drosophila gene expression patterns. Bioinform. 28(12), 16–24 (2012)
Jiang, Y., Ye, G., Chang, S., Ellis, D.P.W., Loui, A.C.: Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: 1st International Conference on Multimedia Retrieval, Trento, Italy, pp. 1–8 (2011)
Ng, Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, Vancouver, Canada, pp. 849–856 (2001)
Kumar, A., Rai, P., III, H.D.: Co-regularized multi-view spectral clustering. In: Annual Conference on Neural Information Processing Systems, Granada, Spain, pp. 1413–1421 (2011)
Luo, S., Zhang, C., Zhang, W., Cao, X.: Consistent and specific multi-view subspace clustering. In: Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pp. 3730–3737 (2018)
Li, X., Zhou, K., Li, C., et al.: Multi-view clustering via neighbor domain correlation learning. Neural Comput. Applic.Applic. 33, 3403–3415 (2021). https://doi.org/10.1007/s00521-020-05185-y
Andrew, G., Arora, R., Bilmes, J.A., Livescu, K.: Deep canonical correlation analysis. In: 30th International Conference on Machine Learning, Atlanta, GA, USA, vol. 28 of JMLR Workshop and Conference Proceedings, pp. 1247–1255 (2013)
Abavisani, M., Patel, V.M.: Deep multimodal subspace clustering networks. J. Sel. Topics Signal Processing 12(6), 1601–1614 (2018)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 62206092), the Natural Science Foundation of Hunan Province (No. 2023JJ40236, 2023JJ40239 and 2022JJ40129), the Research Foundation of Education Bureau of Hunan Province (No. 21B0582, 21B0565 and 21B0572), the Open Project of Xiangjiang Laboratory (No. 22XJ03012,22XJ03014,22XJ03022).
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Li, X., Xiao, Y., Zhang, X., Shi, Q., Tang, X. (2024). ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_18
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