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Tensor projection mechanism and algorithm implementation

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Abstract

This paper analyses the mechanism of tensor projection transformation in depth and introduces a high-efficiency original algorithm developed in a quantum computing language for forward and backward projection between multidimensional tensors and one-dimensional vectors. Additionally, the author compares this algorithm with similar methods from both the Python scientific computing package and other relative development kits in method calls and source code to demonstrate the innovation of the tensor projection algorithm. On this basis, the classical convolution operation program commonly used in machine learning has been parallelized and improved, the analysis algorithm of the Beidou communication satellite view area has been parallelized and improved, and the actual operating efficiency has been greatly improved. After verification, the tensor projection transformation successfully solves the problem of location index mapping of the entity units among different dimensions, can provide a means for optimizing the traditional model of traversal algorithm, and can have significant reference value in eigenspace transformation against a tensor field.

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Data availability

Geological big data sharing and application platform: http://www.creatar.com/show-date-43.html

Key technology research and application demonstration of management and service of field geological survey work based on 3S technology: http://opac.its.csu.edu.cn/NTRdrBookRetr.aspx?strType=text&strKeyValue=

GeoCloud: http://geocloud.cgs.gov.cn/#/portal/collectionInformation/DetailsPage?child_id=102_129332&tableCode=sync_qgg_ajxx

Code availability

Tensor.net: https://github.com/FeiWang93/Tensor-Projection.git

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Acknowledgments

First and foremost, the author would like to show his deepest gratitude to the associate professor of the author’s faculty, Prof. Tuck Wah Leong, a respectable, responsible and resourceful scholar, who has provided the author with valuable guidance in inspiring the writing of this thesis. Without his enlightening instruction, impressive kindness and patience, the author could not have completed his paper. The author extends his thanks to his adviser, Mr. Songlin Sun, for all his encouragement and academic help. The author’s sincere appreciation also goes to the teachers and students at the University of Technology Sydney, who participated in this study with enthusiasm. Finally, the author would like to thank his family and friends for their encouragement and support.

Funding

Financial support from the CGS is greatly acknowledged.

This work was fully supported by a project named “Geological Data Update and Application Service Plan”-“Geological Big Data and Information Service Engineering”-“National Geological Big Data Convergence and Management (2019-2021)”, from the China Geological Survey (CGS) of the Ministry of Natural Resources (Project number: 110101-DD20190387).

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Authors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Fei Wang & Yu Chen

  2. Xi’an Geological Survey Center of the China Geological Survey, Xi’an, 710054, China

    Zhanchang Wang

  3. China Software and Technology Services Co., Ltd, Beijing, 102200, China

    Xiang Li

Authors
  1. Fei Wang
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  2. Zhanchang Wang
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  3. Xiang Li
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  4. Yu Chen
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Contributions

FW wrote the manuscript, derived formulas, and designed tensor projection algorithms; ZW wrote the manuscript, designed quantum computing algorithms, and developed data processing software; XL wrote the manuscript, collected data, and analysed the performance of software; YC wrote the manuscript, processed data, and verified result.

Corresponding author

Correspondence to Zhanchang Wang.

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The authors declare that they have no conflict of interest.

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Wang, F., Wang, Z., Li, X. et al. Tensor projection mechanism and algorithm implementation. Appl Intell 51, 8176–8191 (2021). https://doi.org/10.1007/s10489-021-02332-3

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  • DOI: https://doi.org/10.1007/s10489-021-02332-3

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