A review of approximate methods for kernel-based big media data analysis

Alexandros Iosifidis; Anastasios Tefas; Ioannis Pitas; Moncef Gabbouj

doi:10.1109/EUSIPCO.2016.7760421

A review of approximate methods for kernel-based big media data analysis

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas, Moncef Gabbouj

Department of Electrical & Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

4 Citations (Scopus)

405 Downloads (Pure)

Abstract

With the increasing size of today’s image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.

Original language	English
Title of host publication	2016 24th European Signal Processing Conference (EUSIPCO)
Subtitle of host publication	Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1108-1112
Number of pages	5
ISBN (Electronic)	9780992862657
ISBN (Print)	9781509018918
DOIs	https://doi.org/10.1109/EUSIPCO.2016.7760421
Publication status	Published - Feb 2017
Event	European Signal Processing Conference - Budapest, Hungary Duration: 29 Aug 2016 → 2 Sept 2016

Publication series

Name	Proceedings of the European Signal Processing Conference (EUSIPCO)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)	2076-1465

Conference

Conference	European Signal Processing Conference
Abbreviated title	EUSIPCO2016
Country/Territory	Hungary
City	Budapest
Period	29/08/16 → 2/09/16

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10.1109/EUSIPCO.2016.7760421

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Iosifidis, A., Tefas, A., Pitas, I., & Gabbouj, M. (2017). A review of approximate methods for kernel-based big media data analysis. In 2016 24th European Signal Processing Conference (EUSIPCO): Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary (pp. 1108-1112). (Proceedings of the European Signal Processing Conference (EUSIPCO)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EUSIPCO.2016.7760421

Iosifidis, Alexandros ; Tefas, Anastasios ; Pitas, Ioannis et al. / A review of approximate methods for kernel-based big media data analysis. 2016 24th European Signal Processing Conference (EUSIPCO): Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary. Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 1108-1112 (Proceedings of the European Signal Processing Conference (EUSIPCO)).

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title = "A review of approximate methods for kernel-based big media data analysis",

abstract = "With the increasing size of today{\textquoteright}s image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.",

author = "Alexandros Iosifidis and Anastasios Tefas and Ioannis Pitas and Moncef Gabbouj",

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Iosifidis, A, Tefas, A, Pitas, I & Gabbouj, M 2017, A review of approximate methods for kernel-based big media data analysis. in 2016 24th European Signal Processing Conference (EUSIPCO): Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary. Proceedings of the European Signal Processing Conference (EUSIPCO), Institute of Electrical and Electronics Engineers (IEEE), pp. 1108-1112, European Signal Processing Conference, Budapest, Hungary, 29/08/16. https://doi.org/10.1109/EUSIPCO.2016.7760421

A review of approximate methods for kernel-based big media data analysis. / Iosifidis, Alexandros; Tefas, Anastasios; Pitas, Ioannis et al.
2016 24th European Signal Processing Conference (EUSIPCO): Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1108-1112 (Proceedings of the European Signal Processing Conference (EUSIPCO)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

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AU - Tefas, Anastasios

AU - Pitas, Ioannis

AU - Gabbouj, Moncef

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AB - With the increasing size of today’s image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.

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DO - 10.1109/EUSIPCO.2016.7760421

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509018918

T3 - Proceedings of the European Signal Processing Conference (EUSIPCO)

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Iosifidis A, Tefas A, Pitas I, Gabbouj M. A review of approximate methods for kernel-based big media data analysis. In 2016 24th European Signal Processing Conference (EUSIPCO): Proceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 1108-1112. (Proceedings of the European Signal Processing Conference (EUSIPCO)). Epub 2016 Dec 1. doi: 10.1109/EUSIPCO.2016.7760421

A review of approximate methods for kernel-based big media data analysis

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