Nonnegative PARAFAC2: a flexible coupling approach

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Modeling variability in tensor decomposition methods is one of the challenges of source separation. One possible solution to account for variations from one data set to another, jointly analysed, is to resort to the PARAFAC2 model. However, so far imposing constraints on the mode with variability has not been possible. In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode. An algorithm to compute the proposed flexible PARAFAC2 model is derived, and its performance is studied on both synthetic and chemometrics data.

OriginalsprogEngelsk
TitelLatent Variable Analysis and Signal Separation : 14th International Conference, LVA/ICA 2018, Proceedings
RedaktørerYannick Deville, Sharon Gannot, Russell Mason, Mark D. Plumbley, Dominic Ward
Antal sider10
ForlagSpringer
Publikationsdato2018
Sider89-98
ISBN (Trykt)978-3-319-93763-2
ISBN (Elektronisk)978-3-319-93764-9
DOI
StatusUdgivet - 2018
Begivenhed14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018 - Guildford, Storbritannien
Varighed: 2 jul. 20185 jul. 2018

Konference

Konference14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018
LandStorbritannien
ByGuildford
Periode02/07/201805/07/2018
NavnLecture notes in computer science
Vol/bind10891
ISSN0302-9743

Links

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