Memory efficient low-rank non-linear subspace tracking

F. Sheikholeslami, D. Berberidis, G. B. Giannakis

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

1 Scopus citations

Abstract

The task of low-rank subspace tracking is of paramount importance for feature extraction over streaming data. Considering the broad range of applications in which the data fail to adhere to a linear model, the present work proposes a nonlinear subspace tracking algorithm. The proposed algorithm can effectively learn and track an evolving non-linear subspace in an online fashion. The notion of non-linearity is accommodated via exploitation of kernel-induced mappings, whose computational as well as memory requirements, if untreated, will impose scalability issues in large datasets. This issue is addressed by imposing a predefined affordable budget on the number of data vectors to be stored, preventing computational and memory growth of the algorithm, while enabling the tracking of possibly evolving subspaces. Numerical tests corroborate the effectiveness of the proposed algorithm on synthetic as well as real datasets.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Keywords

  • Nonlinear subspace tracking
  • budgeted learning
  • kernel methods
  • online learning

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