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arXiv · 1701.02058

Coupled Compound Poisson Factorization

Mehmet E. Basbug·Barbara E. Engelhardt
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Abstract

We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model and, as examples of our framework, implement three different data-generating models---a mixture model, linear regression, and factor analysis---to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data mechanism substantially improves the quality of the results, as measured using data log likelihood on a held-out test set.

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Mehmet E. Basbug, Barbara E. Engelhardt. 2017-01-09. Coupled Compound Poisson Factorization. https://arxiv.org/abs/1701.02058

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