Multi-view feature extraction is an efficient approach for alleviating the issue of dimensionality in highdimensional multi-view data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. Most CL-based methods were constructed only from the sample level. In this study, we propose a novel multiview feature extraction method based on dual contrastive head, which introduce structural-level contrastive loss into sample-level CL-based method. Structural-level CL push the potential subspace structures consistent in any two cross views, which assists sample-level CL to extract discriminative features more effectively. Furthermore, it is proven that the relationships between structural-level CL and mutual information and probabilistic intraand inter-scatter, which provides the theoretical support for the excellent performance. Finally, numerical experiments on six real datasets demonstrate the superior performance of the proposed method compared to existing methods.

翻译：多视角特征提取是缓解高维多视角数据维度问题的高效方法。对比学习作为一种流行的自监督学习方法，近年来备受关注。现有基于对比学习的方法大多仅从样本层面构建。本研究提出了一种基于双对比头结构的新型多视角特征提取方法，在样本级对比学习方法中引入结构级对比损失。结构级对比学习能促使任意两个交叉视角中的潜在子空间结构保持一致，从而辅助样本级对比学习更有效地提取判别性特征。此外，本文证明了结构级对比学习与互信息及概率类内/类间散度之间的内在联系，这为该方法的优异性能提供了理论支撑。最后，在六个真实数据集上的数值实验表明，与现有方法相比，所提方法具有更优越的性能。