A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Generalized vec trick for fast learning of pairwise kernel models
Tekijät: Viljanen Markus, Airola Antti, Pahikkala Tapio
Kustantaja: SPRINGER
Julkaisuvuosi: 2022
Journal: Machine Learning
Tietokannassa oleva lehden nimi: MACHINE LEARNING
Lehden akronyymi: MACH LEARN
Vuosikerta: 111
Numero: 2
Aloitussivu: 543
Lopetussivu: 573
Sivujen määrä: 31
ISSN: 0885-6125
eISSN: 1573-0565
DOI: https://doi.org/10.1007/s10994-021-06127-y
Verkko-osoite: https://link.springer.com/article/10.1007/s10994-021-06127-y
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/69083033
Preprintin osoite: https://arxiv.org/abs/2009.01054
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 << n. In this work we show how all the reviewed kernels can be expressed as sums of Kronecker products, allowing the use of generalized vec trick for speeding up their computation. In the experiments, we demonstrate how the introduced approach allows scaling pairwise kernels to much larger data sets than previously feasible, and provide an extensive comparison of the kernels on a number of biological interaction prediction tasks.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
