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Limited memory bundle DC algorithm for sparse pairwise kernel learning

Author

Listed:
  • Napsu Karmitsa

    (University of Turku)

  • Kaisa Joki

    (University of Turku)

  • Antti Airola

    (University of Turku)

  • Tapio Pahikkala

    (University of Turku)

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.

Suggested Citation

  • Napsu Karmitsa & Kaisa Joki & Antti Airola & Tapio Pahikkala, 2025. "Limited memory bundle DC algorithm for sparse pairwise kernel learning," Journal of Global Optimization, Springer, vol. 92(1), pages 55-85, May.
    • Handle: RePEc:spr:jglopt:v:92:y:2025:i:1:d:10.1007_s10898-025-01481-w
    DOI: 10.1007/s10898-025-01481-w
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