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The xyz algorithm for fast interaction search in high-dimensional data

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When performing regression on a dataset with p variables, it is often of interest to go beyond using main effects and include interactions as products between individual variables. However, if the number of variables p is large, as is common in genomic datasets, the computational cost of searching through O(p2) interactions can be prohibitive. In this talk I will introduce a new randomised algorithm called xyz that is able to discover interactions with high probability and under mild conditions has a runtime that is subquadratic in p. The underlying idea is to transform interaction search into a much simpler close pairs of points problem. We will see how strong interactions can be discovered in almost linear time, whilst finding weaker interactions requires O(pu) operations for 1

This talk is part of the Isaac Newton Institute Seminar Series series.

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