A Faster without Scarifying Accuracy Online Decomposition Approach for Higher-Order Tensors

Xu, Y; Gu, Y; Do, DMQ; Xie, H; Qing, L

A Faster without Scarifying Accuracy Online Decomposition Approach for Higher-Order Tensors

Xu, Y Gu, Y Do, DMQ Xie, H Qing, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017, 2017, 2018-January pp. 159 - 166
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Xu, Y	en_US
dc.contributor.author	Gu, Y	en_US
dc.contributor.author	Do, DMQ	en_US
dc.contributor.author	Xie, H	en_US
dc.contributor.author	Qing, L	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017, 2017, 2018-January pp. 159 - 166	en_US
dc.identifier.isbn	9781538622094	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127359
dc.description.abstract	© 2017 IEEE. Tensors could be very suitable for representing multidimensional data. In recent years, CANDECOMP/PARAFAC (CP) decomposition which is one of the most popular methods for Multidimensional Data Analysis has been widely studied and extensively applied. However, today's datasets will often change dynamically, and the amount of data is showing a trend of exponential growth. It is a very necessary and difficult task to perform a CP decomposition on a dynamically changing tensor with very large scale growth. The traditional and classic methods, such as Alternating Least Squares (ALS) algorithm, cannot be directly used to the dynamical tensor due to their huge consumption of time and memory. In addition, the existing online CP methods can only partially solve this problem and can only be applied to thirdorder tensor. Based on the online CP method, we proposed a simplified online CP decomposition algorithm that can be a good solution to these problems. It not only has the similar decomposition accuracy rate with ALS algorithm but also the decomposition speed faster than the ALS algorithm hundreds of thousands of times. Comparing with other state-of-theart online CP methods, it has better decomposition quality and decomposition speed. The experimental results of four methods show that, our approach reduces computational time significantly without scarifying accuracy. our approach has a similar accuracy rate, and the speed has increased by tens times than online CP decomposition. Even in some datasets, the speed and accuracy of our approach are both better than the other approach.	en_US
dc.relation.ispartof	Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017	en_US
dc.relation.isbasedon	10.1109/CyberC.2017.22	en_US
dc.title	A Faster without Scarifying Accuracy Online Decomposition Approach for Higher-Order Tensors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0805 Distributed Computing	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 Computer Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Tensors could be very suitable for representing multidimensional data. In recent years, CANDECOMP/PARAFAC (CP) decomposition which is one of the most popular methods for Multidimensional Data Analysis has been widely studied and extensively applied. However, today's datasets will often change dynamically, and the amount of data is showing a trend of exponential growth. It is a very necessary and difficult task to perform a CP decomposition on a dynamically changing tensor with very large scale growth. The traditional and classic methods, such as Alternating Least Squares (ALS) algorithm, cannot be directly used to the dynamical tensor due to their huge consumption of time and memory. In addition, the existing online CP methods can only partially solve this problem and can only be applied to thirdorder tensor. Based on the online CP method, we proposed a simplified online CP decomposition algorithm that can be a good solution to these problems. It not only has the similar decomposition accuracy rate with ALS algorithm but also the decomposition speed faster than the ALS algorithm hundreds of thousands of times. Comparing with other state-of-theart online CP methods, it has better decomposition quality and decomposition speed. The experimental results of four methods show that, our approach reduces computational time significantly without scarifying accuracy. our approach has a similar accuracy rate, and the speed has increased by tens times than online CP decomposition. Even in some datasets, the speed and accuracy of our approach are both better than the other approach.

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http://hdl.handle.net/10453/127359