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Cognition Math Based on Factor Space

Author

Listed:
  • Peizhuang Wang

    (Liaoning Technical University)

  • He Ouyang

    (Jie Macroelectronics Co. Ltd.)

  • Yixin Zhong

    (Beijing University of Posts and Telecommunication)

  • Huacan He

    (Northwestern Polytechnic University)

Abstract

The core of big data is intelligence still. Facing the challenge of big data, AI needs a deep and united theory, especially, a deep and united cognition math. There were three branches of cognition math emerging in 1982. One of them is Factor space theory initiated by the first author. Factor is factor, i.e. the initiator of fact, the quality-root of things, which is the generalization of gene. Factor space is the coordinate space with dimensions named by factors, which is generalization of Cartesian coordination for describing things and thinking. The paper introduces how to emulate cognition functions by factor space and how clear and pertinent the emulation is. Four simple and fast algorithms are presented. Based on factor space, the cognition packet is built as the basic unit in factor databases. Different from the existent data processing, factor databases are built by cultivation, whose target is cultivating the sample S of background relation R to emulate R. With the lapse of time, the background sample S becomes more mature and stable. Once the S equals to R, cognition packet will have the whole correct knowledge. Maintaining such a powerful function for big data, factor databases can employ background base to drastically compress data without information loss. As for the existent data processing frightened by the multi-challenge of big data, factor space theory brings us a sedative. The tide of big data will be tamed in factor databases. The cultivation is easy to be made since the sample of background relation don’t concern about privacy. The bottlenecks caused by big data can be overcome by factor space theory, which is the best framework for cognition math.

Suggested Citation

  • Peizhuang Wang & He Ouyang & Yixin Zhong & Huacan He, 2016. "Cognition Math Based on Factor Space," Annals of Data Science, Springer, vol. 3(3), pages 281-303, September.
    • Handle: RePEc:spr:aodasc:v:3:y:2016:i:3:d:10.1007_s40745-016-0084-x
    DOI: 10.1007/s40745-016-0084-x
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    Citations

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    Cited by:

    1. Anda Tang & Pei Quan & Lingfeng Niu & Yong Shi, 2022. "A Survey for Sparse Regularization Based Compression Methods," Annals of Data Science, Springer, vol. 9(4), pages 695-722, August.
    2. Tiejun Cui & Peizhuang Wang & Shasha Li, 2022. "Research on Uncertainty of System Function State from Factors-Data-Cognition," Annals of Data Science, Springer, vol. 9(3), pages 593-609, June.
    3. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
    4. Xingsen Li & Junlin Zeng & Haitao Liu & Peizhuang Wang, 2022. "Intelligent Problem Solving Model and its Cross Research Directions Based on Factor Space and Extenics," Annals of Data Science, Springer, vol. 9(3), pages 469-484, June.
    5. Perkiss, Stephanie & Bernardi, Cristiana & Dumay, John & Haslam, Jim, 2021. "A sticky chocolate problem: Impression management and counter accounts in the shaping of corporate image," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 81(C).
    6. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
    7. Hui Sun & Fanhui Zeng & Yang Yang, 2022. "Covert Factor’s Exploiting and Factor Planning," Annals of Data Science, Springer, vol. 9(3), pages 449-467, June.
    8. Binxiang Jiang, 2022. "Research on Factor Space Engineering and Application of Evidence Factor Mining in Evidence-based Reconstruction," Annals of Data Science, Springer, vol. 9(3), pages 503-537, June.

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