Nicolas Seichepine

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This work introduces a new framework for nonnegative matrix factorization (NMF) in multisensor or multimodal data configurations, where taking into account the mutual dependence that exists between the related parallel streams of data is expected to improve performance. In contrast with previous works that focused on co-factorization methods -where some(More)
In this paper we propose a non-negative matrix factorization (NMF) model with piecewise-constant activation coefficients. This structure is enforced using a total variation penalty on the rows of the activation matrix. The resulting optimization problem is solved with a majorization-minimization procedure. The proposed algorithm is well suited to analyze(More)
This paper presents a new method for bimodal nonnegative matrix factorization (NMF). This method is well-suited to situations where two streams of data are concurrently analyzed and are expected to be related by loosely common factors. It allows for a soft co-factorization, which takes into account the relationship that exists between the modalities being(More)
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