Generalized vec trick for fast learning of pairwise kernel models

Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target or protein-protein interactions, or customer-product preferences. In this work, we present a comprehensive review of pair...

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Bibliographic Details
Published in:arXiv.org 2022-02
Main Authors: Viljanen, Markus, Airola, Antti, Pahikkala, Tapio
Format: Article
Language:English
Subjects:
Algorithms
Cartesian coordinates
Kernel functions
Polynomials
Predictions
Proteins
Supervised learning
Training
Online Access:Get full text
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Summary:Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target or protein-protein interactions, or customer-product preferences. In this work, we present a comprehensive review of pairwise kernels, that have been proposed for incorporating prior knowledge about the relationship between the objects. Specifically, we consider the standard, symmetric and anti-symmetric Kronecker product kernels, metric-learning, Cartesian, ranking, as well as linear, polynomial and Gaussian kernels. Recently, a O(nm + nq) time generalized vec trick algorithm, where n, m, and q denote the number of pairs, drugs and targets, was introduced for training kernel methods with the Kronecker product kernel. This was a significant improvement over previous O(n^2) training methods, since in most real-world applications m,q
ISSN:2331-8422
DOI:10.48550/arxiv.2009.01054