Compressive Online Decomposition of Dynamic Signals Via N-ℓ1 Minimization With Clustered Priors

Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer, Andre Kaup

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We introduce a compressive online decomposition via solving an n-ℓ1 cluster-weighted minimization to decompose a sequence of data vectors into sparse and low-rank components. In contrast to conventional batch Robust Principal Component Analysis (RPCA)-which needs to access full data-our method processes a data vector of the sequence per time instance from a small number of measurements. The n-ℓ1 cluster-weighted minimization promotes (i) the structure of the sparse components and (ii) their correlation with multiple previously-recovered sparse vectors via clustering and re-weighting iteratively. We establish guarantees on the number of measurements required for successful compressive decomposition under the assumption of slowly-varying low-rank components. Experimental results show that our guarantees are sharp and the proposed algorithm outperforms the state of the art.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE Workshop on Statistical Signal Processing (SSP)
PublisherIEEE
Publication date2018
Pages846-50
ISBN (Print)978-1-5386-1570-3
DOIs
Publication statusPublished - 2018
Event2018 IEEE Workshop on Statistical Signal Processing (SSP) - Freiburg, Germany
Duration: 10 Jun 201813 Jun 2018

Conference

Conference2018 IEEE Workshop on Statistical Signal Processing (SSP)
CountryGermany
CityFreiburg
Period10/06/201813/06/2018

Keywords

  • Robust PCA
  • Sparse signal
  • Low-rank model
  • Cluster-weighted minimization
  • Prior information

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