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. In this study, we propose a novel multi-view feature extraction method based on triple contrastive heads, which combines the sample-, recovery- , and feature-level contrastive losses to extract the sufficient yet minimal subspace discriminative information in compliance with information bottleneck principle. In MFETCH, we construct the feature-level contrastive loss, which removes the redundent information in the consistency information to achieve the minimality of the subspace discriminative information. Moreover, the recovery-level contrastive loss is also constructed in MFETCH, which captures the view-specific discriminative information to achieve the sufficiency of the subspace discriminative information.The numerical experiments demonstrate that the proposed method offers a strong advantage for multi-view feature extraction.

翻译：多视图特征提取是缓解高维多视图数据维数灾难的有效方法。对比学习作为一种广受欢迎的自监督学习方法，近来引起了广泛关注。本研究提出了一种基于三重对比头的新型多视图特征提取方法，该方法融合了样本级、恢复级和特征级对比损失，依据信息瓶颈原理提取充分且最小的子空间判别信息。在MFETCH中，我们构建了特征级对比损失，通过去除一致性信息中的冗余信息来实现子空间判别信息的最小化。此外，MFETCH还构建了恢复级对比损失，用于捕获视图特定的判别信息，从而实现子空间判别信息的充分性。数值实验表明，所提方法在多视图特征提取方面具有显著优势。