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Introduction

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Robust Latent Feature Learning for Incomplete Big Data

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Abstract

In the era of Big Data, information explosion is very common in our daily life [1–4]. For instance, Google and Flickr generate more than 20 PB and 3.6 TB data per day, respectively [5]. According to the prediction of the International Data Corporation, the global data sum will go to 175 ZB by 2025 [6]. Therefore, how to effectively and efficiently mine the desired valuable information from large-scale data has become a crucial challenge and has attracted much attention all over the world [7–11].

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References

  1. Xindong, W., Zhu, X., Gong-Qing, W., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  2. Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 51(1), 1–35 (2019)

    Article  Google Scholar 

  3. Lu, R., Jin, X., Zhang, S., Qiu, M., Wu, X.: A study on big knowledge and its engineering issues. IEEE Trans. Knowl. Data Eng. 31(9), 1630–1644 (2019)

    Article  Google Scholar 

  4. Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Information Fusion. 42, 146–157 (2018)

    Article  Google Scholar 

  5. Patrizio, A.: Idc: expect 175 zettabytes of data worldwide by 2025. Network World from IDC. (2018). https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html

  6. Athey, S.: Beyond prediction: using big data for policy problems. Science. 355(6324), 483–485 (2017)

    Article  Google Scholar 

  7. Di, W., Shang, M., Luo, X., Ji, X., Yan, H., Deng, W., Wang, G.: Self-training semi-supervised classification based on density peaks of data. Neurocomputing. 275, 180–191 (2018)

    Article  Google Scholar 

  8. D. Wu, M. Shang, G. Wang, and L. Li. A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution. 2018 IEEE 15th international conference on networking, Sensing and Control (ICNSC), 1-6, (2018)

    Google Scholar 

  9. Xuegang, H., Zhou, P., Li, P., Wang, J., Xindong, W.: A survey on online feature selection with streaming features. Front. Comp. Sci. 12(3), 479–493 (2018)

    Article  Google Scholar 

  10. Z. Yu, D. Wu, and Y. He. A robust latent factor analysis model for incomplete data recovery in wireless sensor networks. 2022 IEEE International Conference on Edge Computing and Communications (EDGE)), 178–183, (2022)

    Google Scholar 

  11. Chen, J., Wang, R., Wu, D., Luo, X.: A differential evolution-enhanced position-transitional approach to latent factor analysis. IEEE Transactions on Emerging Topics in Computational Intelligence, 1–13 (2022)

    Google Scholar 

  12. Deng, S., Chen, F., Di, W., He, Y., Ge, H., Ge, Y.: Quantitative combination load forecasting model based on forecasting error optimization. Comput. Electr. Eng. 101, 108125 (2022)

    Article  Google Scholar 

  13. He, Y., Baijun, W., Di, W., Beyazit, E., Chen, S., Xindong, W.: Online learning from capricious data streams: a generative approach. Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019, 2491–2497 (2019)

    Google Scholar 

  14. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature selection: a data perspective. ACM Computing Surveys (CSUR). 50 (6), 94 (2018)

    Google Scholar 

  15. Xiaoyu, X., Pang, G., Di, W., Shang, M.: Joint hyperbolic and euclidean geometry contrastive graph neural networks. Inf. Sci. 609, 799–815 (2022)

    Article  Google Scholar 

  16. Alelyani, S., Tang, J., Liu, H.: Feature Selection for Clustering: a Review’: ‘Data Clustering, pp. 29–60. Chapman and Hall/CRC (2018)

    Google Scholar 

  17. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Computers & Electrical Engineering. 40(1), 16–28 (2014)

    Article  Google Scholar 

  18. Jiliang Tang, Salem Alelyani, and Huan Liu: ‘Feature selection for classification: A review’: ‘Data classification: Algorithms and applications’ (2014), pp. 37–64

    Google Scholar 

  19. Xindong, W., Kui, Y., Ding, W., Wang, H., Zhu, X.: Online feature selection with streaming features. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1178–1192 (2013)

    Article  Google Scholar 

  20. Ege Beyazit, Jeevithan Alagurajah, and Xindong Wu. Online learning from data streams with varying feature spaces. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence), 3232–3239, (2019)

    Google Scholar 

  21. Xiaoyu, X., Di, W., Shang, M.: A structure-characteristic-aware network embedding model via differential evolution. Expert Syst. Appl. 204, 117611 (2022)

    Article  Google Scholar 

  22. Wu, L., Sun, P., Hong, R., Ge, Y., Wang, M.: Collaborative neural social recommendation. IEEE Trans. Syst. Man Cybern. Systems, 1–13 (2018)

    Google Scholar 

  23. Antoine Boutet, Davide Frey, Rachid Guerraoui, Arnaud Jegou, and Anne Marie Kermarrec. Whatsup: A decentralized instant news recommender. In 27th International Parallel and Distributed Processing Symposium (IPDPS)), 741–752, (2013)

    Google Scholar 

  24. Steffen Rendle, Walid Krichene, Li Zhang, and John R. Anderson. Neural collaborative filtering vs. Matrix factorization revisited. In Proceedings of the 14th ACM Conference on Recommender Systems, Rec Sys, 240–248, (2020)

    Google Scholar 

  25. Hong-Jian Xue, Xin-Yu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 3203–3209, (2017)

    Google Scholar 

  26. Dong, X., Lei, Y., Zhonghuo, W., Sun, Y., Yuan, L., Zhang, F.: A hybrid collaborative filtering model with deep structure for recommender systems. In proceedings of Thirty-First AAAI Conference on Artificial Intelligence. 1309-1315 (2017)

    Google Scholar 

  27. Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM), 153–162, (2016)

    Google Scholar 

  28. Koren, Y., Bell, R.: Advances in Collaborative Filtering’: ‘Recommender Systems Handbook, pp. 77–118. Springer (2015)

    Book  Google Scholar 

  29. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook’: ‘Recommender Systems Handbook, pp. 1–35. Springer (2011)

    MATH  Google Scholar 

  30. P. Zhang, Y. He, and D. Wu. An ensemble latent factor model for highly accurate web service qos prediction. 2021 IEEE International Conference on Big Knowledge (ICBK)), 361–368, (2021)

    Google Scholar 

  31. D. Wu, G. Lu, and Z. Xu. Robust and accurate representation learning for high-dimensional and sparse matrices in recommender systems. 2020 IEEE International Conference on Knowledge Graph (ICKG)), 489–496, (2020)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer and Information Science, Southwest University, Chongqing, China

    Di Wu

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  1. Di Wu
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Wu, D. (2023). Introduction. In: Robust Latent Feature Learning for Incomplete Big Data. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-8140-1_1

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  • DOI: https://doi.org/10.1007/978-981-19-8140-1_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8139-5

  • Online ISBN: 978-981-19-8140-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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