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Communication Dans Un Congrès Année : 2022

Coupled tensor factorization for flow cytometry data analysis

Résumé

In this paper, we propose a new method for automated flow cytometry data analysis. By modeling a multidimensional probability distribution as a mixture of simpler distributions, we can reformulate the problem as a coupled tensor approximation of 3D marginals. In order to reduce the computational load, we use partially coupled strategies. We also propose a grouping of rank-one components together with a new visualization of the results. We demonstrate the usefulness of the proposed methodology on simulated and real data.
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Dates et versions

hal-03718437 , version 1 (08-07-2022)

Identifiants

  • HAL Id : hal-03718437 , version 1

Citer

Philippe Flores, Guillaume Harlé, Anne-Béatrice Notarantonio, Konstantin Usevich, Maud d'Aveni, et al.. Coupled tensor factorization for flow cytometry data analysis. 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022, Aug 2022, Xi'an, China. ⟨hal-03718437⟩
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