Fast Kronecker product kernel methods via generalized vec trick
This work addresses a computational bottleneck for zero-shot learning applications like drug-target interaction prediction, though it is incremental as it extends existing methods to more general graph structures.
The paper tackles the problem of efficiently training Kronecker product kernel methods for graph data with non-complete training graphs, generalizing the vec trick to enable accurate models with order-of-magnitude improvements in training and prediction time.
Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the training data, a setting known as zero-shot or zero-data learning. Such a setting occurs in numerous applications, including drug-target interaction prediction, collaborative filtering and information retrieval. Efficient training algorithms based on the so-called vec trick, that makes use of the special structure of the Kronecker product, are known for the case where the training data is a complete bipartite graph. In this work we generalize these results to non-complete training graphs. This allows us to derive a general framework for training Kronecker product kernel methods, as specific examples we implement Kronecker ridge regression and support vector machine algorithms. Experimental results demonstrate that the proposed approach leads to accurate models, while allowing order of magnitude improvements in training and prediction time.