Abstract
Gaussian mixture model (GMM) is a well-known model-based approach for data clustering. However, when the data samples are insufficient, the classical GMM-based clustering algorithms are not effective anymore. Referring to the idea of transfer clustering methods, this paper proposes a general transfer GMM-based clustering framework, which employs the important knowledge extracted from some known source domain to guide and improve the clustering on the target domain with small-scale data. Specifically, three traditional GMM-based clustering approaches are extended to the corresponding transfer clustering versions. Furthermore, to avoid the negative transfer problem, maximum mean discrepancy (MMD) is introduced to search the most matched source domain to provide more positive guidance for data clustering on the target domain. Experiments on synthetic and real-world datasets demonstrate the efficiency of the presented framework compared with several existing transfer clustering algorithms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andrews, J.L.: Addressing overfitting and underfitting in gaussian model-based clustering. Comput. Stat. Data Anal. 127, 160–171 (2018)
Baktashmotlagh, M., Harandi, M., Lovell, B., Salzmann, M.: Unsupervised domain adaptation by domain invariant projection. In: 2013 IEEE International Conference on Computer Vision, pp. 769–776 (2013)
Chatzis, S.: A method for training finite mixture models under a fuzzy clustering principle. Fuzzy Sets Syst. 161(23), 3000–3013 (2010)
Lai, C.Y., Yang, M.S.: Entropy-type classification maximum likelihood algorithms for mixture models. Soft Comput. 15(2), 373–381 (2011)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Self-taught clustering. In: Proceedings of the 25th International Conference on Machine Learning, pp. 200–207 (2008)
Dang, B., Zhou, J., Wang, R., Wang, L., Han, S., Chen, Y.: Transfer learning based kernel fuzzy clustering. In: 2019 International Conference on Fuzzy Theory and Its Applications (iFUZZY), pp. 21–25 (2019)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39, 1–22 (1977)
Deng, Z., Jiang, Y., Chung, F.L., Choi, K.S., Wang, S.: Transfer prototype-based fuzzy clustering. IEEE Trans. Fuzzy Syst. 24(5), 1210–1232 (2014)
Du, M., Ding, S., Xue, Y., Shi, Z.: A novel density peaks clustering with sensitivity of local density and density-adaptive metric. Knowl. Inf. Syst. 59, 1–25 (2018)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel two-sample test. J. Mach. Learn. Res. 13(25), 723–773 (2012)
Gupta, M., Sinha, A.: Recursive density-based hierarchical clustering in gaussian distributed sensor network. Int. J. Syst. Assurance Eng. Manag. (2020)
Hang, W., Chung, F.I., Wang, S.: Transfer affinity propagation-based clustering. Inf. Sci. 348, 337–356 (2016)
He, X., Cai, D., Shao, Y., Bao, H., Han, J.: Laplacian regularized gaussian mixture model for data clustering. IEEE Trans. Knowl. Data Eng. 23(9), 1406–1418 (2011)
Iqbal, S., Zhang, C.: A new hesitant fuzzy-based forecasting method integrated with clustering and modified smoothing approach. Int. J. Fuzzy Syst. 22(3), 1104–1117 (2020)
Jiang, W., Chung, F.L.: Transfer spectral clustering. In: European Conference on Machine Learning & Knowledge Discovery in Databases, pp. 789–803 (2012)
Jiang, W., Liu, W., Chung, Fl: Knowledge transfer for spectral clustering. Pattern Recognit. 81, 484–496 (2018)
Ju, Z., Liu, H.: Fuzzy gaussian mixture models. Pattern Recognit. 45(3), 1146–1158 (2012)
Kannan, S.: Intelligent object recognition in underwater images using evolutionary-based gaussian mixture model and shape matching. Signal Image Video Process. 1–9 (2020)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 2200–2207 (2013)
Ma, J., Wang, T.: Entropy penalized automated model selection on gaussian mixture. Int. J. Pattern Recognit. Artif. Intell. 18(8), 1501–1512 (2004)
McLachlan, G.J., Basford, K.E.: Mixture models: inference and applications to clustering. Inference Appl. Clust. 38(2) (1988)
Memon, K.H., Memon, S., Qureshi, M.A., Alvi, M.B., Kumar, D., Shah, R.A.: Kernel possibilistic fuzzy c-means clustering with local information for image segmentation. Int. J. Fuzzy Syst. 21(1), 321–332 (2019)
NENE, S.A.: Columbia object image library(coil-20). Technical Report 5 (1996)
Qian, P., Jiang, Y., Deng, Z., Hu, L., Sun, S., Wang, S., Muzic, R.: Cluster prototypes and fuzzy memberships jointly leveraged cross-domain maximum entropy clustering. IEEE Trans. Cybern. 46(1), 181–193 (2016)
Quost, B., Denœux, T.: Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets Syst. 286, 134–156 (2016)
Reddy, C.K., Chiang, H.D., Rajaratnam, B.: Trust-tech-based expectation maximization for learning finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1146–1157 (2008)
Saranya, S., Poonguzhali, S., Karunakaran, S.: Gaussian mixture model based clustering of manual muscle testing grades using surface electromyogram signals. Phys. Eng. Sci. Med. (2020)
Sevillano, X., Socoró, J.C., Alías, F.: Parallel hierarchical architectures for efficient consensus clustering on big multimedia cluster ensembles. Inf. Sci. 511, 212–228 (2020)
Sharma, R., Verma, K.: Fuzzy shared nearest neighbor clustering. Int. J. Fuzzy Syst. 21(6), 2667–2678 (2019)
Tran, D., Wagner, M.: Fuzzy entropy clustering. In: Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No.00CH37063), vol. 1, pp. 152–157 (2000)
Wang, Y., Dong, J., Zhou, J., Xu, G., Chen, Y.: Random feature map-based multiple kernel fuzzy clustering with all feature weights. Int. J. Fuzzy Syst. 21(7), 2132–2146 (2019)
Wolfe, J.: Object Cluster Analysis of Social Areas. University of California, Berkeley (1963)
Xu, G., Zhou, J., Dong, J., Zhang, T., Chen, L., Han, S., Wang, L., Chen, Y.: Multivariate morphological reconstruction based fuzzy clustering with a weighting multi-channel guided image filter for color image segmentation. Int. J. Mach. Learn. Cybern. (2020). https://doi.org/10.1007/s13042-020-01151-1
Yang, M.S., Chang-Chien, S.J., Nataliani, Y.: Unsupervised fuzzy model-based gaussian clustering. Inf. Sci. 481, 1–23 (2019)
Yang, Z., Shrivastava, A.K., Leung, T.K.: Regularized gaussian mixture model for high-dimensional clustering. IEEE Trans. Cybern. 49(10), 3677–3688 (2019)
Yeganegi, H., Salami, P., Daliri, mr: A template-based sequential algorithm for online clustering of spikes in extracellular recordings. Cogn. Comput. 12(2), 542–552 (2020)
Yu, L., Dang, Y., Yang, G.: Transfer clustering via constraints generated from topics. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3203–3208 (2012)
Zhao, X., Li, Y., Zhao, Q.: A fuzzy clustering approach for complex color image segmentation based on gaussian model with interactions between color planes and mixture gaussian model. Int. J. Fuzzy Syst. 20(1), 309–317 (2018)
Zhou, J., Chen, L., Chen, C.L.P., Wang, Y., Li, H.: Uncertain data clustering in distributed peer-to-peer networks. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2392–2406 (2018)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants with No. 61873324, No. 61903156, and No. 61872419, the Natural Science Foundation of Shandong Province under Grant with No. ZR2019MF040 and No. ZR2018LF005.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, R., Zhou, J., Jiang, H. et al. A General Transfer Learning-based Gaussian Mixture Model for Clustering. Int. J. Fuzzy Syst. 23, 776–793 (2021). https://doi.org/10.1007/s40815-020-01016-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-020-01016-3