Optimal Feature Extraction and Classification of Tensors via Matrix Product State Decomposition

Bengua, JA; Phien, HN; Tuan, HD

Optimal Feature Extraction and Classification of Tensors via Matrix Product State Decomposition

Bengua, JA

Phien, HN Tuan, HD

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 2015, pp. 669 - 672
Issue Date:: 2015-08-17

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Field	Value	Language
dc.contributor.author	Bengua, JA https://orcid.org/0000-0001-9375-0449	en_US
dc.contributor.author	Phien, HN	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.date.issued	2015-08-17	en_US
dc.identifier.citation	Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 2015, pp. 669 - 672	en_US
dc.identifier.isbn	9781467372787	en_US
dc.identifier.uri	http://hdl.handle.net/10453/45785
dc.description.abstract	© 2015 IEEE. Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large tensors. The novelty of the paper is the introduction of a single core tensor obtained from the MPS that not only contains a significantly reduced feature space, but can perform classification with high accuracy without the need of feature selection methods.	en_US
dc.relation.ispartof	Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015	en_US
dc.relation.isbasedon	10.1109/BigDataCongress.2015.105	en_US
dc.title	Optimal Feature Extraction and Classification of Tensors via Matrix Product State Decomposition	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large tensors. The novelty of the paper is the introduction of a single core tensor obtained from the MPS that not only contains a significantly reduced feature space, but can perform classification with high accuracy without the need of feature selection methods.

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http://hdl.handle.net/10453/45785