Gap filling in the plant kingdom - Trait prediction using hierarchical probabilistic matrix factorization

Hanhuai Shan, Jens Kattge, Peter B. Reich, Arindam Banerjee, Franziska Schrodt, Markus Reichstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Plant traits are a key to understanding and predicting the adaptation of ecosystems to environmental changes, which motivates the TRY project aiming at constructing a global database for plant traits and becoming a standard resource for the ecological community. Despite its unprecedented coverage, a large percentage of missing data substantially constrains joint trait analysis. Meanwhile, the trait data is characterized by the hierarchical phylogenetic structure of the plant kingdom. While factorization based matrix completion techniques have been widely used to address the missing data problem, traditional matrix factorization methods are unable to leverage the phylogenetic structure. We propose hierarchical probabilistic matrix factorization (HPMF), which effectively uses hierarchical phylogenetic information for trait prediction. We demonstrate HPMF's high accuracy, effectiveness of incorporating hierarchical structure and ability to capture trait correlation through experiments.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1303-1310
Number of pages8
StatePublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume2

Other

Other29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

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    Shan, H., Kattge, J., Reich, P. B., Banerjee, A., Schrodt, F., & Reichstein, M. (2012). Gap filling in the plant kingdom - Trait prediction using hierarchical probabilistic matrix factorization. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012 (pp. 1303-1310). (Proceedings of the 29th International Conference on Machine Learning, ICML 2012; Vol. 2).