Factor Space, the Theoretical Base of Data Science

被引：12

作者：

Wang P.-Z. ^{[1
]}

Liu Z.-L. ^{[2
]}

Shi Y. ^{[3
]}

Guo S.-C. ^{[1
]}

机构：

[1] College of Intelligence Engineering and Mathematics, Liaoning Technical University, Fuxin, 123000, Liaoning

[2] National Defense University PLA China, Beijing

[3] Research Center of Fictitious Economy and Data Science, Chinese Academy of Science, Beijing

来源：

Annals of Data Science | 2014年 / 1卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Background relation; Factor space; Factor vane; Factorial databases; Factorial neural networks; Information fusion; Sample cultivation;

D O I：

10.1007/s40745-014-0017-5

中图分类号：

学科分类号：

摘要：

This paper introduces factor space theory, which provides a general coordinate system to describe the real world and a theoretical base for data science. Based on the theory, factorial databases is presented, which carries a new kind of statistics to do intelligent analysis for coming tide of Big Data. © 2014, Springer-Verlag Berlin Heidelberg.

引用

页码：233 / 251

页数：18

共 36 条

[31]

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[32]

Wang P.Z., Guo S., Bao Y.K., Liu HT Causality analysis in factor spaces, J Liaoning Eng Technol Univ, 33, 7, pp. 1-6

[33]

Wang H.D., Wang P.Z., Guo S.C., Improved Factor Analysis Algorithm on Factor Spaces, (2014)

[34]

Bao Y.K., Ru H.Y., Jin S.J., Factorial Analysis, a New Algorithm of Knowledge Mining in Causality Space, (2014)

[35]

Codd E.F., A relational model of data for large shared data banks, Commun ACM, 13, 6, pp. 377-387, (1970)

[36]

Wang P.Z., Discussions on Hirsch conjecture and existence of strongly polynomial-time simplex variants, Ann Data Sci, 1, 1, pp. 41-71, (2014)

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