Limited memory bundle DC algorithm for sparse pairwise kernel learning Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.1007/s10898-025-01481-w
· OA: W4409160764
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, $$\ell _1$$ - and $$\ell _0$$ -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.
