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arXiv · 1901.01568

Learning Nonlinear Mixtures: Identifiability and Algorithm

Bo Yang·Xiao Fu·Nicholas D. Sidiropoulos·Kejun Huang
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Abstract

Linear mixture models have proven very useful in a plethora of applications, e.g., topic modeling, clustering, and source separation. As a critical aspect of the linear mixture models, identifiability of the model parameters is well-studied, under frameworks such as independent component analysis and constrained matrix factorization. Nevertheless, when the linear mixtures are distorted by an unknown nonlinear functions -- which is well-motivated and more realistic in many cases -- the identifiability issues are much less studied. This work proposes an identification criterion for a nonlinear mixture model that is well grounded in many real-world applications, and offers identifiability guarantees. A practical implementation based on a judiciously designed neural network is proposed to realize the criterion, and an effective learning algorithm is proposed. Numerical results on synthetic and real-data corroborate effectiveness of the proposed method.

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Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Kejun Huang. 2019-01-06. Learning Nonlinear Mixtures: Identifiability and Algorithm. https://doi.org/10.1109/tsp.2020.2989551

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