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A comparative study of pairwise learning methods based on kernel ridge regression

(2018) NEURAL COMPUTATION. 30(8). p.2245-2283
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Abstract
Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze urdversality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.
Keywords
DRUG-TARGET INTERACTIONS, SUPPORT VECTOR MACHINES, LEAST-SQUARES, REGULARIZATION ALGORITHMS, INTERACTION PREDICTION, MATRIX FACTORIZATION, NETWORKS

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Please use this url to cite or link to this publication:

MLA
Stock, Michiel, Tapio Pahikkala, Antti Airola, et al. “A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.” NEURAL COMPUTATION 30.8 (2018): 2245–2283. Print.
APA
Stock, M., Pahikkala, T., Airola, A., De Baets, B., & Waegeman, W. (2018). A comparative study of pairwise learning methods based on kernel ridge regression. NEURAL COMPUTATION, 30(8), 2245–2283.
Chicago author-date
Stock, Michiel, Tapio Pahikkala, Antti Airola, Bernard De Baets, and Willem Waegeman. 2018. “A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.” Neural Computation 30 (8): 2245–2283.
Chicago author-date (all authors)
Stock, Michiel, Tapio Pahikkala, Antti Airola, Bernard De Baets, and Willem Waegeman. 2018. “A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.” Neural Computation 30 (8): 2245–2283.
Vancouver
1.
Stock M, Pahikkala T, Airola A, De Baets B, Waegeman W. A comparative study of pairwise learning methods based on kernel ridge regression. NEURAL COMPUTATION. 2018;30(8):2245–83.
IEEE
[1]
M. Stock, T. Pahikkala, A. Airola, B. De Baets, and W. Waegeman, “A comparative study of pairwise learning methods based on kernel ridge regression,” NEURAL COMPUTATION, vol. 30, no. 8, pp. 2245–2283, 2018.
@article{8578969,
  abstract     = {Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze urdversality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.},
  author       = {Stock, Michiel and Pahikkala, Tapio and Airola, Antti and De Baets, Bernard and Waegeman, Willem},
  issn         = {0899-7667},
  journal      = {NEURAL COMPUTATION},
  keywords     = {DRUG-TARGET INTERACTIONS,SUPPORT VECTOR MACHINES,LEAST-SQUARES,REGULARIZATION ALGORITHMS,INTERACTION PREDICTION,MATRIX FACTORIZATION,NETWORKS},
  language     = {eng},
  number       = {8},
  pages        = {2245--2283},
  title        = {A comparative study of pairwise learning methods based on kernel ridge regression},
  url          = {http://dx.doi.org/10.1162/neco_a_01096},
  volume       = {30},
  year         = {2018},
}

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