Fast Kronecker product kernel methods via generalized vec trick

01/07/2016
by   Antti Airola, et al.
0

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.

READ FULL TEXT

page 2

page 12

research
08/04/2020

Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription

We present an end-to-end, interpretable, deep-learning architecture to l...
research
03/05/2018

A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression

Many machine learning problems can be formulated as predicting labels fo...
research
02/29/2020

An End-to-End Graph Convolutional Kernel Support Vector Machine

A novel kernel-based support vector machine (SVM) for graph classificati...
research
09/30/2022

Efficient Graph based Recommender System with Weighted Averaging of Messages

We showcase a novel solution to a recommendation system problem where we...
research
09/02/2020

Generalized vec trick for fast learning of pairwise kernel models

Pairwise learning corresponds to the supervised learning setting where t...
research
11/21/2016

An Efficient Training Algorithm for Kernel Survival Support Vector Machines

Survival analysis is a fundamental tool in medical research to identify ...

Please sign up or login with your details

Forgot password? Click here to reset