Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

A comparative study of pairwise learning methods based on Kernel ridge regression




Julkaisun tekijät: Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman

Kustantaja: MIT Press Journals

Julkaisuvuosi: 2018

Journal: Neural Computation

Tietokannassa oleva lehden nimi: Neural Computation

Volyymi: 30

Julkaisunumero: 8

Sivujen määrä: 39

ISSN: 0899-7667

eISSN: 1530-888X

DOI: http://dx.doi.org/10.1162/neco_a_01096

Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/35696344


Tiivistelmä

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.


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Last updated on 2022-07-04 at 16:59