Online subspace learning and nonlinear classification of Big Data with misses

Fatemeh Sheikholesalmi, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

'Big Data' classification is hindered by the large volume of often high-dimensional data, missing or absent features and, in streaming operation, the need for real-time processing. This paper aims at learning a kernelized support-vector-machine (SVM) classifier from (generally nonlinearly separable) large-scale incomplete data 'on the fly.' Leveraging the low-rank attribute of the (even incomplete) data matrix, a novel online algorithm is developed for tracking the latent linear subspace jointly with the nonlinear classifier. Tailored for big data applications, dimensionality reduction based on the learned subspace is carried out online, while at the same time seeking the classifier in the reduced dimension. Performance analysis along with preliminary tests corroborate the effectiveness of the novel approach.

Original languageEnglish (US)
Title of host publication2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479984282
DOIs
StatePublished - Apr 15 2015
Event2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 - Baltimore, United States
Duration: Mar 18 2015Mar 20 2015

Publication series

Name2015 49th Annual Conference on Information Sciences and Systems, CISS 2015

Other

Other2015 49th Annual Conference on Information Sciences and Systems, CISS 2015
CountryUnited States
CityBaltimore
Period3/18/153/20/15

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