Limited memory bundle DC algorithm for sparse pairwise kernel learning

Napsu Karmitsa (), Kaisa Joki (), Antti Airola () and Tapio Pahikkala ()
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Napsu Karmitsa: University of Turku
Kaisa Joki: University of Turku
Antti Airola: University of Turku
Tapio Pahikkala: University of Turku

Journal of Global Optimization, 2025, vol. 92, issue 1, No 3, 55-85

Abstract: Abstract Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this paper, we formulate the pairwise learning problem as a difference of convex (DC) optimization problem using the Kronecker product kernel, $$\ell _1$$ ℓ 1 - and $$\ell _0$$ ℓ 0 -regularizations, and various, possibly nonsmooth, loss functions. Our aim is to develop an efficient learning algorithm, SparsePKL, that produces accurate predictions with the desired sparsity level. In addition, we propose a novel limited memory bundle DC algorithm (LMB-DCA) for large-scale nonsmooth DC optimization and apply it as an underlying solver in the SparsePKL. The performance of the SparsePKL-algorithm is studied in seven real-world drug-target interaction data and the results are compared with those of the state-of-art methods in pairwise learning.

Keywords: Pairwise kernel learning; Nonsmooth DC optimization; Bundle methods; DCA; $$\ell _0$$ ℓ 0 -pseudo-norm; Zero-shot learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10898-025-01481-w

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