A new algorithm for multimodal soft coupling

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Sedighin, F

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Type of Document: Article
DOI: 10.1007/978-3-319-53547-0_16
Publisher: Springer Verlag , 2017
Abstract:
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. © Springer International Publishing AG 2017
Keywords:
Bayesian framework ; Nonnegative matrix factorization ; Soft coupling ; Factorization ; Bayesian frameworks ; Multi-modal ; Multi-modal data ; Nonnegative matrix factorization ; Probabilistic modeling ; Synthetic data ; Matrix algebra
Source: 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, 21 February 2017 through 23 February 2017 ; Volume 10169 LNCS , 2017 , Pages 162-171 ; 03029743 (ISSN); 9783319535463 (ISBN)
URL: https://link.springer.com/chapter/10.1007/978-3-319-53547-0_16