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A New Algorithm for Multimodal Soft Coupling

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)

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.

Keywords

Nonnegative matrix factorization Bayesian framework Soft coupling 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringSharif University of TechnologyTehranIran
  2. 2.GIPSA-lab, CNRS, Univ. Grenoble Alpes, Grenoble INPGrenobleFrance

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