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

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas, Moncef Gabbouj

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

4 Citations (Scopus)
374 Downloads (Pure)


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 languageEnglish
Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
Subtitle of host publicationProceedings of a meeting held 29 August - 2 September 2016, Budapest, Hungary
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9780992862657
ISBN (Print)9781509018918
Publication statusPublished - Feb 2017
EventEuropean Signal Processing Conference - Budapest, Hungary
Duration: 29 Aug 20162 Sept 2016

Publication series

NameProceedings of the European Signal Processing Conference (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO2016


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