Abstract
In the era of big data, a large number of multimodal data are constantly generated and accumulated. In the practical application analysis of multimodal big data, the modal incompleteness, real-time processing, modal imbalance and high-dimensional attributes pose severe challenges to the multimodal fusion method. In view of the above characteristics of multimodal data and the shortcomings of existing multimodal fusion algorithms, this chapter studies the multimodal data fusion algorithms from four aspects: incomplete modal analysis fusion, incremental modal clustering fusion, heterogeneous modal migration fusion and low dimensional modal sharing fusion. A series of multimodal data fusion models and algorithms are designed.
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References
Wang, Y., Jin, X., Cheng, X.: Network big data: present and future. Chin. J. Comput. 36(6), 1125–1138 (2013)
Manyika, J., Chui, M., Brown, B., et al.: Big Data: The Next Frontier for Innovation, Competition, and Productivity, vol. 5(33), pp. 1–137 (2011)
Li, J., Liu, X.: An important aspect of big data: data usability. J. Comput. Res. Dev. 50(6), 1147–1162 (2013)
Zhang, Q.: Research on deep computation model for big data feature learning. Dalian University of Technology, Dalian (2015)
Gao, Q., Zhang, F., Wang, R., et al.: Trajectory big data: a review of key technologies in data processing. J. Softw. 28(4), 959–992 (2017)
Li, H., Wang, Y., Jia, Y., et al.: Network big data oriented knowledge fusion methods: a survey. Chin. J. Comput. 2017(1), 1–27 (2017)
Wu, X., Zhu, X., Wu, G.Q., et al.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)
Zhao, L., Chen, Z., Hu, Y., et al.: Distributed feature selection for efficient economic big data analysis. IEEE Trans. Big Data. (2016). https://doi.org/10.1109/TBDATA.2016.2601934
Du, X., Chen, Y.: Approaches for value extraction on big data. Big Data Res. 3(2), 19–25 (2017)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Lahat, D., Adali, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE 103(9), 1449–1477 (2015)
Xing, J., Niu, Z., Huang, J., et al.: Towards robust and accurate multi-view and partially-occluded face alignment. IEEE Trans. Pattern Anal. Mach. Intell. (2017). https://doi.org/10.1109/TPAMI.2017.2697958
Zhang, C., Hu, Q., Fu, H., et al.: Latent multi-view subspace clustering. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4333–4341. IEEE, Honolulu (2017)
Liu, J., Wang, C., Gao, J., et al.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 252–260. Philadelphia (2013)
Li, S.Y., Jiang, Y., Zhou, Z.H.: Partial multi-view clustering. In: Proceedings of 28th AAAI Conference on Artificial Intelligence, pp. 1968–1974. AI Access Foundation, Quebec City (2014)
Yin, Q., Wu, S., Wang, L.: Unified subspace learning for incomplete and unlabeled multi-view data. Pattern Recogn. 67(67), 313–327 (2017)
Cai, D., He, X., Han, J., et al.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1548–1560 (2011)
Wang, W., Arora, R., Livescu, K., et al.: On deep multi-view representation learning: objectives and optimization. arXiv preprint. arXiv: 1602.01024 (2016)
Guan, Z., Zhang, L., Peng, J., et al.: Multi-view concept learning for data representation. IEEE Trans. Knowl. Data Eng. 27(11), 3016–3028 (2015)
Zhao, L., Chen, Z., Yang, Z., et al.: Local similarity imputation based on fast clustering for incomplete data in cyber-physical systems. IEEE Syst. J. https://doi.org/10.1109/JSYST.2016.2576026 (2016)
Meng, L., Tan, A.H., Xu, D.: Semi-supervised heterogeneous fusion for multimedia data co-clustering. IEEE Trans. Knowl. Data Eng. 26(9), 2293–2306 (2014)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Pereira, J.C., Coviello, E., Doyle, G., et al.: On the role of correlation and abstraction in cross-modal multimedia retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 521–535 (2014)
Shu, X., Qi, G.J., Tang, J., et al.: Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 35–44. ACM, Brisbane (2015)
Shao, W., He, L., Lu, C.T., et al.: Online unsupervised multi-view feature selection. In: Proceedings of 16th IEEE International Conference on Data Mining, pp. 1203–1208. IEEE, Barcelona (2016)
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Chen, Z., Zhao, L., Li, Q., Song, X., Zhang, J. (2022). Multimodal Data Fusion. In: Nicopolitidis, P., Misra, S., Yang, L.T., Zeigler, B., Ning, Z. (eds) Advances in Computing, Informatics, Networking and Cybersecurity. Lecture Notes in Networks and Systems, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-87049-2_3
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DOI: https://doi.org/10.1007/978-3-030-87049-2_3
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