Coupled Tensor Factorization for Flow Cytometry Data Analysis
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)(2022)
Abstract
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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Key words
Flow cytometry,Naive Bayes model,Coupled tensor factorization
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