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Conference Paper

Uncertainty quantified matrix completion using Bayesian Hierarchical Matrix factorization


Kattge,  Jens
TRY: Global Initiative on Plant Traits, Dr. J. Kattge, Department Biogeochemical Processes, Prof. S. E. Trumbore, Max Planck Institute for Biogeochemistry, Max Planck Society;


Schrodt,  Franziska
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Fazayeli, F., Banerjee, A., Kattge, J., Schrodt, F., & Reich, P. B. (2014). Uncertainty quantified matrix completion using Bayesian Hierarchical Matrix factorization. In 13th International Conference on Machine Learning and Applications (ICMLA) (pp. 312-317). Piscataway: IEEE. doi:10.1109/ICMLA.2014.56.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-9996-C
Low-rank matrix completion methods have been successful in a variety of settings such as recommendation systems. However, most of the existing matrix completion methods only provide a point estimate of missing entries, and do not characterize uncertainties of the predictions. In this paper, we propose a Bayesian hierarchical probabilistic matrix factorization (BHPMF) model to 1) incorporate hierarchical side information, and 2) provide uncertainty quantified predictions. The former yields significant performance improvements in our target application domain of plant trait prediction by leveraging the taxonomic hierarchy in the plant kingdom. The latter is helpful in identifying predictions of low confidence which can in turn be used to guide field work for data collection efforts. A Gibbs sampler is designed for inference in the model. Further, we propose a multiple inheritance BHPMF (MI-BHPMF) which can work with a general directed acyclic graph (DAG) structured hierarchy, rather than a tree. We present comprehensive experimental results and analysis on the problem of plant trait prediction, a key problem in ecology, using the largest database of plant traits, where BHPMF shows strong empirical performance in uncertainty quantified trait prediction, outperforming the state-of-the-art based on point estimates. Further, we show that BHPMF is more accurate when it is confident, whereas the error is high when the uncertainty is high.