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Pairwise learning or dyadic prediction concerns the prediction of propertiesfor pairs of objects. It can be seen as an umbrella covering various machinelearning problems such as matrix completion, collaborative filtering,multi-task learning, transfer learning, network prediction and zero-shotlearning. In this work we analyze kernel-based methods for pairwise learning,with a particular focus on a recently-suggested two-step method. We show thatthis method offers an appealing alternative for commonly-appliedKronecker-based methods that model dyads by means of pairwise featurerepresentations and pairwise kernels. In a series of theoretical results, weestablish correspondences between the two types of methods in terms of linearalgebra and spectral filtering, and we analyze their statistical consistency.In addition, the two-step method allows us to establish novel algorithmicshortcuts for efficient training and validation on very large datasets. Puttingthose properties together, we believe that this simple, yet powerful method canbecome a standard tool for many problems. Extensive experimental results for arange of practical settings are reported.

Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method