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Stability-based PAC-Bayes analysis for multi-view learning algorithms

Sun, S; Yu, M; Shawe-Taylor, J; Mao, L; (2022) Stability-based PAC-Bayes analysis for multi-view learning algorithms. Information Fusion , 86-87 pp. 76-92. 10.1016/j.inffus.2022.06.006.

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Abstract

Multi-view learning exploits structural constraints among multiple views to effectively learn from data. Although it has made great methodological achievements in recent years, the current generalization theory is still insufficient to prove the merit of multi-view learning. This paper blends stability into multi-view PAC-Bayes analysis to explore the generalization performance and effectiveness of multi-view learning algorithms. We propose a novel view-consistency regularization to produce an informative prior that helps to obtain a stability-based multi-view bound. Furthermore, we derive an upper bound on the stability coefficient that is involved in the PAC-Bayes bound of multi-view regularization algorithms for the purpose of computation, taking the multi-view support vector machine as an example. Experiments provide strong evidence on the advantageous generalization bounds of multi-view learning over single-view learning. We also explore strengths and weaknesses of the proposed stability-based bound compared with previous non-stability multi-view bounds experimentally.

Type: Article
Title: Stability-based PAC-Bayes analysis for multi-view learning algorithms
DOI: 10.1016/j.inffus.2022.06.006
Publisher version: https://doi.org/10.1016/j.inffus.2022.06.006
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Multi-view Learning, PAC-Bayes Analysis, Stability, Generalization
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10153299
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