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

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2018

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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
StatePublished - 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
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

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