A general-purpose toolbox for efficient Kronecker-based learning

Abstract

<p>Pairwise learning is a machine learning paradigm where the goal is to predict properties of pairs of objects. Applications include recommender systems, molecular network inference, and ecological interaction prediction. Kronecker-based learning systems provide a simple yet elegant method to learn from such pairs. Using tricks from linear algebra, these models can be trained, tuned, and validated on large datasets. Our Julia package Kronecker.jl aggregates these shortcuts and efficient algorithms using a lazily evaluated Kronecker product ‘⊗’, such that it is easy to experiment with learning algorithms using the Kronecker product.<br /></p>