Multi-view Feature Extraction based on Dual Contrastive Head

• URL: http://arxiv.org/abs/2302.03932v1
• Date: Wed, 8 Feb 2023 08:13:17 GMT
• Title: Multi-view Feature Extraction based on Dual Contrastive Head
• Authors: Hongjie Zhang
• Abstract summary: We propose a novel multiview feature extraction method based on dual contrastive head. It introduces structural-level contrastive loss into sample-level CL-based method. Numerical experiments on six real datasets demonstrate the superior performance of the proposed method.
• Score: 1.4467794332678539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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