Abstract
Learning from the absent multiple sources data is very challenging, especially when lacking label information. Although existing multiple kernel learning methods with kernel imputing achieved remarkable performance, they may fail in practical issues due to hardly tracking the data with huge missing samples or easily fall into a local optimum. Thus, we propose an unsupervised absent multiple kernel extreme learning machine to effectively deal with absent data. It firstly extracts information from absent multiple sources data via multiple kernels to structure a new data space named K-space. Then, the optimal multiple kernel combination coefficients are learned in the K-space. Finally, we establish a seamlessly integrated object function to complete the absent kernel matrices and acquire the clustering information simultaneously by a well-designed optimization strategy within finite steps. As evidenced by comprehensive experiments, the proposed method provides significantly better clustering performance.
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References
Zhao, B., Kwok, J.T., Zhang, C.: Multiple kernel clustering. In: SDM, pp. 638–649 (2009)
Gnen, M., Alpaydn, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)
Yu, S., Tranchevent, L., Liu, X., Glnzel, W., Suykens, J.A.K., De Moor, B., Moreau, Y.: Optimized data fusion for kernel k-means clustering. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 1031–1039 (2012)
Liu, X., Dou, Y., Yin, J., Wang, L., Zhu, E.: Multiple kernel k-means clustering with matrix-induced regularization. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1888–1894 (2016)
Du, L., Zhou, P., Shi, L., Wang, H., Fan, M., Wang, W., Shen, Y.D.: Robust multiple kernel k-means using 2;1 -norm. In: International Conference on Artificial Intelligence, pp. 3476–3482 (2015)
Lu, Y., Wang, L., Lu, J., Yang, J., Shen, C.: Multiple kernel clustering based on centered kernel alignment. Pattern Recogn. 47(11), 3656–3664 (2014)
Li, M., Liu, X., Wang, L., Dou, Y., Yin, J., Zhu, E.: Multiple kernel clustering with local kernel alignment maximization. In: International Joint Conference on Artificial Intelligence, pp. 1704–1710 (2016)
Zhang, L., Hu, X.: Locally adaptive multiple kernel clustering. Neurocomputing 137, 192–197 (2014)
Margolin, A.A.: Localized data fusion for kernel k-means clustering with application to cancer biology. In: International Conference on Neural Information Processing Systems, pp. 1305–1313 (2014)
Xiang, L., Zhao, G., Li, Q., Hao, W., Li, F.: A fast unsupervised heterogeneous data learning approach. IEEE Access 24, 35305–35315 (2018)
Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A., Ye, J.: Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1149–1157. ACM (2012)
Chechik, G., Heitz, G., Elidan, G., Abbeel, P., Koller, D.: Max-margin classification of data with absent features. J. Mach. Learn. Res. 9(Jan), 1–21 (2008)
Parker, R.: Missing Data Problems in Machine Learning. VDM Verlag (2010)
Ghahramani, Z.: Supervised learning from incomplete data via an EM approach. Adv. Neural Inf. Process. Syst. 6, 120–127 (1994)
Chang, X., Tao, D., Chao, X.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24(12), 5812–5825 (2015)
Bhadra, S., Kaski, S., Rousu, J.: Multi-view kernel completion. Mach. Learn. 1–27 (2016)
Liu, X., Li, M., Wang, L., Dou, Y., Yin, J., Zhu, E.: Multiple kernel k-means with incomplete kernels. In: Association for the Advancement of Artificial Intelligence (2017)
Zhu, C., Liu, X., Liu, Q., Ming, Y., Yin, J.: Distance based multiple kernel ELM: a fast multiple kernel learning approach. Math. Probl. Eng. 2015(6), 1–9 (2015)
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Xiang, L., Zhao, G., Li, Q., Zhu, Z. (2020). Unsupervised Absent Multiple Kernel Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_26
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DOI: https://doi.org/10.1007/978-3-030-23307-5_26
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