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Reducing data storage requirements for machine learning algorithms using principle component analysis

Reducing data storage requirements for machine learning algorithms using principle component analysis

Kinkiri, Saritha and Melis, Wim J. C. ORCID: 0000-0003-3779-8629 (2016) Reducing data storage requirements for machine learning algorithms using principle component analysis. In: International Conference on Applied System Innovation (ICASI 2016). IEEE/Taiwanese Institute of Knowledge Innovation (TIKI), Taiwan, Okinawa, Japan. ISBN 978-1-4673-9889-3 (doi:10.1109/ICASI.2016.7539804)

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Abstract

While current computers have shown to be particular useful for arithmetic and logic implementations, their accuracy and efficiency for applications such as e.g. face, object and speech recognition, are not that impressive, especially when compared to what the human brain can do. Machine learning algorithms have been useful, especially for these type of applications, as they operate in a similar way to the human brain, by learning the data provided and storing it for future recognition. Until now, there has been a strong focus on developing the process of data storage and retrieval, merely neglecting the value of the provided information and the amount of data required to store. Hence, currently all information provided is stored, because it is difficult for the machine to decide which information needs to be stored. Consequently, large amounts of data are stored, which then affects the processing of the data. Thus, this paper investigates the opportunity to reduce data storage through the use of differentiation and combine it with an existing similarity detection algorithm. The differentiation is achieved through the use of, Principal Component Analysis (PCA), which not only reduces the data storage requirements by about 80%, but also improves the overall detection accuracy around 50 to nearly 80%.

Item Type: Conference Proceedings
Title of Proceedings: International Conference on Applied System Innovation (ICASI 2016)
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Conference took place from 28 May to 1st June 2016 at Okinawa Conference Centre, Okinawa, Japan.
Uncontrolled Keywords: Machine Learning, Principle Component Analysis, Data Storage Efficiency
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Related URLs:
Last Modified: 19 Jul 2017 15:45
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/14800

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