Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays. (arXiv:2010.02469v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2010.02469

Unmeasured or latent variables are often the cause of correlations between
multivariate measurements, which are studied in a variety of fields such as
psychology, ecology, and medicine. For Gaussian measurements, there are
classical tools such as factor analysis or principal component analysis with a
well-established theory and fast algorithms. Generalized Linear Latent Variable
models (GLLVMs) generalize such factor models to non-Gaussian responses.
However, current algorithms for estimating model parameters in GLLVMs require
intensive computation and do not scale to large …

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