Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization

2012/06/27 by Hanhuai Shan, Jens Kattge, Shan, Hanhuai +10 · 2 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #Computational Engineering #FOS: Computer and information sciences #Finance #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Plant and animal studies #and Science (cs.CE) #cs.CE #cs.LG #stat.AP

paper · pdf · doi:10.48550/arxiv.1206.6439

Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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