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Multi-view feature extraction is an efficient approach for alleviating theissue of dimensionality in highdimensional multi-view data. Contrastivelearning (CL), which is a popular self-supervised learning method, has recentlyattracted considerable attention. Most CL-based methods were constructed onlyfrom the sample level. In this study, we propose a novel multiview featureextraction method based on dual contrastive head, which introducestructural-level contrastive loss into sample-level CL-based method.Structural-level CL push the potential subspace structures consistent in anytwo cross views, which assists sample-level CL to extract discriminativefeatures more effectively. Furthermore, it is proven that the relationshipsbetween structural-level CL and mutual information and probabilistic intraandinter-scatter, which provides the theoretical support for the excellentperformance. Finally, numerical experiments on six real datasets demonstratethe superior performance of the proposed method compared to existing methods.

Multi-view Feature Extraction based on Dual Contrastive Head