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

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. • We propose a novel view-consistency regularization to deduce PAC-Bayes bounds. • We upper-bound the stability coefficient of MvSVM to compute specific bounds. • Experimental results validate the theoretical superiority of multi-view learning. • The new bounds show good tightness and ability to support model selection. [ABSTRACT FROM AUTHOR]