View in EDS HTML Full Text PDF Full Text

Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach.

Saved in:
Bibliographic Details
Title: Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach.
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]
Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 193364600
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Predicting pairwise interaction affinities with ℓ<subscript>0</subscript>-penalized least squares–a nonsmooth bi-objective optimization based approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Paasivirta%2C+Pauliina%22">Paasivirta, Pauliina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Numminen%2C+Riikka%22">Numminen, Riikka</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Airola%2C+Antti%22">Airola, Antti</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Karmitsa%2C+Napsu%22">Karmitsa, Napsu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> napsu@karmitsa.fi</i><br /><searchLink fieldCode="AR" term="%22Pahikkala%2C+Tapio%22">Pahikkala, Tapio</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Optimization+Methods+%26+Software%22">Optimization Methods & Software</searchLink>. Apr2026, Vol. 41 Issue 2, p450-477. 28p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Nonsmooth+optimization%22">Nonsmooth optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Kronecker+products%22">Kronecker products</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193364600
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10556788.2023.2280784
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 450
    Subjects:
      – SubjectFull: Nonsmooth optimization
        Type: general
      – SubjectFull: Least squares
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Kronecker products
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
    Titles:
      – TitleFull: Predicting pairwise interaction affinities with ℓ0-penalized least squares–a nonsmooth bi-objective optimization based approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Paasivirta, Pauliina
      – PersonEntity:
          Name:
            NameFull: Numminen, Riikka
      – PersonEntity:
          Name:
            NameFull: Airola, Antti
      – PersonEntity:
          Name:
            NameFull: Karmitsa, Napsu
      – PersonEntity:
          Name:
            NameFull: Pahikkala, Tapio
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10556788
          Numbering:
            – Type: volume
              Value: 41
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Optimization Methods & Software
              Type: main
ResultId 1