A new algorithm for multimodal soft coupling

Farnaz Sedighin, Massoud Babaie-Zadeh, Bertrand Rivet, Christian Jutten

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

Аннотация

In this paper, the problem of multimodal soft coupling under the Bayesian framework when variance of probabilistic model is unknown is investigated. Similarity of shared factors resulted from Nonnegative Matrix Factorization (NMF) of multimodal data sets is controlled in a soft manner by using a probabilistic model. In previous works, it is supposed that the probabilistic model and its parameters are known. However, this assumption does not always hold. In this paper it is supposed that the probabilistic model is already known but its variance is unknown. So the proposed algorithm estimates the variance of the probabilistic model along with the other parameters during the factorization procedure. Simulation results with synthetic data confirm the effectiveness of the proposed algorithm.

Язык оригиналаАнглийский
Название основной публикацииLatent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
РедакторыPetr Tichavsky, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadege Thirion-Moreau
ИздательSpringer Verlag
Страницы162-171
Число страниц10
ISBN (печатное издание)9783319535463
DOI
СостояниеОпубликовано - 2017
Опубликовано для внешнего пользованияДа
Событие13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017 - Grenoble, Франция
Продолжительность: 21 февр. 201723 февр. 2017

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том10169 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

Конференция

Конференция13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017
Страна/TерриторияФранция
ГородGrenoble
Период21/02/1723/02/17

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