TY - GEN
T1 - Uncertainty quantified matrix completion using bayesian hierarchical matrix factorization
AU - Fazayeli, Farideh
AU - Banerjee, Arindam
AU - Kattge, Jens
AU - Schrodt, Franziska
AU - Reich, Peter B.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/5
Y1 - 2014/2/5
N2 - 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 the problem of plant trait prediction, a key problem in ecology, 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 on the problem of plant trait prediction 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.
AB - 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 the problem of plant trait prediction, a key problem in ecology, 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 on the problem of plant trait prediction 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.
KW - Bayesian Analysis
KW - Probabilistic Matrix Factorization
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=84946687210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946687210&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2014.56
DO - 10.1109/ICMLA.2014.56
M3 - Conference contribution
AN - SCOPUS:84946687210
T3 - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
SP - 312
EP - 317
BT - Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
A2 - Ferri, Cesar
A2 - Qu, Guangzhi
A2 - Chen, Xue-wen
A2 - Wani, M. Arif
A2 - Angelov, Plamen
A2 - Lai, Jian-Huang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Y2 - 3 December 2014 through 6 December 2014
ER -