The Union of Intersections (UoI) Method for Interpretable Data Driven Discovery and Prediction
Wednesday 08th of June 2016 at 12:00pm
The increasing size and complexity of scientific data could dramatically enhance basic scientific discovery and prediction for applications. Realizing this potential requires novel statistical analysis algorithms that are both interpretable and predictive. We introduce the Union of Intersections (UoI) method, a flexible, modular, and scalable paradigm for regression and classification. UoI satisfies the bicriteria of accurate recovery of a small number of interpretable features while maintaining high-quality prediction accuracy. We describe UoI and summarize new theoretical results on its mechanics. We evaluate UoI on synthetic and real biomedical data, demonstrating its superior performance. On real data, we demonstrate: extraction of interpretable functional networks from human electrophysiology recordings, accurate prediction of phenotypes from genotype-phenotype data with reduced features, and improved prediction parsimony on several benchmark biomedical data sets for classification. These results suggest that UoI could improve interpretation in data-driven discovery and prediction across scientific fields.
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