A New Algorithm for Multimodal Soft Coupling

Abstract : In this paper, the problem of multimodal soft coupling under the Bayesian framework when the variance of the probabilistic model is unknown is investigated. Similarity of shared factors resulted from Non-negative Matrix Factorization (NMF) of multimodal data sets is imposed in a soft manner by using a probabilistic model. In previous works, it is supposed that this probabilistic model is exactly 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 other parameters during the factorization procedure. Simulation results with synthetic data confirm the effectiveness of the proposed algorithm.
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Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lectures Notes in Computer Sciences (10169), pp.162 - 171, 2017, Latent Variable Analysis and Signal Separation. 〈10.1007/978-3-319-53547-0_16〉
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Farnaz Sedighin, Massoud Zadeh, Bertrand Rivet, Christian Jutten. A New Algorithm for Multimodal Soft Coupling. Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lectures Notes in Computer Sciences (10169), pp.162 - 171, 2017, Latent Variable Analysis and Signal Separation. 〈10.1007/978-3-319-53547-0_16〉. 〈hal-01479306〉

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