Tensor projection mechanism and algorithm implementation

Wang, F; Wang, Z; Li, X; Chen, Y

Tensor projection mechanism and algorithm implementation

Wang, F Wang, Z Li, X Chen, Y

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Applied Intelligence, 2021, 51, (11), pp. 8176-8191
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Wang, F
dc.contributor.author	Wang, Z
dc.contributor.author	Li, X
dc.contributor.author	Chen, Y https://orcid.org/0000-0001-5110-4203
dc.date.accessioned	2022-04-28T22:03:43Z
dc.date.available	2022-04-28T22:03:43Z
dc.date.issued	2021-11-01
dc.identifier.citation	Applied Intelligence, 2021, 51, (11), pp. 8176-8191
dc.identifier.issn	0924-669X
dc.identifier.issn	1573-7497
dc.identifier.uri	http://hdl.handle.net/10453/156769
dc.description.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.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Applied Intelligence
dc.relation.isbasedon	10.1007/s10489-021-02332-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Tensor projection mechanism and algorithm implementation
dc.type	Journal Article
utslib.citation.volume	51
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-28T22:03:40Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	51
utslib.citation.issue	11

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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http://hdl.handle.net/10453/156769