Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach.
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| Title: | Predicting pairwise interaction affinities with ℓ |
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| Authors: | Paasivirta, Pauliina1 (AUTHOR), Numminen, Riikka2 (AUTHOR), Airola, Antti2 (AUTHOR), Karmitsa, Napsu2 (AUTHOR) napsu@karmitsa.fi, Pahikkala, Tapio2 (AUTHOR) |
| Source: | Optimization Methods & Software. Apr2026, Vol. 41 Issue 2, p450-477. 28p. |
| Subjects: | Nonsmooth optimization, Least squares, Machine learning, Multi-objective optimization, Kronecker products, Mathematical optimization |
| Abstract: | In this paper, we introduce a novel nonsmooth optimization-based method LMBM-Kron $ \ell _0 $ ℓ 0 LS for solving large-scale pairwise interaction affinity prediction problems. The aim of LMBM-Kron $ \ell _0 $ ℓ 0 LS is to produce accurate predictions using as sparse a model as possible. We apply the least squares approach with Kronecker product kernels for a loss function and a continuous formulation of $ \ell _0 $ ℓ 0 pseudonorm for regularization. Thus, we end up solving a nonsmooth optimization problem. In addition, we apply a specific bi-objective criterion to strike a balance between the prediction accuracy of the learned model and the sparsity of the obtained solution. We compare LMBM-Kron $ \ell _0 $ ℓ 0 LS with some state-of-the-art methods using three benchmark and two simulated data sets under four distinct experimental settings, including zero-shot learning. Moreover, both binary and continuous interaction affinity labels are considered with LMBM-Kron $ \ell _0 $ ℓ 0 LS. The results show that LMBM-Kron $ \ell _0 $ ℓ 0 LS finds sparse solutions without sacrificing too much in the prediction performance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this paper, we introduce a novel nonsmooth optimization-based method LMBM-Kron $ \ell _0 $ ℓ 0 LS for solving large-scale pairwise interaction affinity prediction problems. The aim of LMBM-Kron $ \ell _0 $ ℓ 0 LS is to produce accurate predictions using as sparse a model as possible. We apply the least squares approach with Kronecker product kernels for a loss function and a continuous formulation of $ \ell _0 $ ℓ 0 pseudonorm for regularization. Thus, we end up solving a nonsmooth optimization problem. In addition, we apply a specific bi-objective criterion to strike a balance between the prediction accuracy of the learned model and the sparsity of the obtained solution. We compare LMBM-Kron $ \ell _0 $ ℓ 0 LS with some state-of-the-art methods using three benchmark and two simulated data sets under four distinct experimental settings, including zero-shot learning. Moreover, both binary and continuous interaction affinity labels are considered with LMBM-Kron $ \ell _0 $ ℓ 0 LS. The results show that LMBM-Kron $ \ell _0 $ ℓ 0 LS finds sparse solutions without sacrificing too much in the prediction performance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10556788 |
| DOI: | 10.1080/10556788.2023.2280784 |
