# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
This approach achieved state-of-the-art results on the DDAD and nuScenes datasets. Similarly, [PERSON] et al. [26] introduced CanonPose, a self-supervised monocular 3D human pose estimation method that learns 3D pose from unlabeled multi-view images by enforcing multi-view consistency and unbiased reconstruction, thereby eliminating the need for camera calibration and 3D ground truth. [PERSON] et al. [27] proposed SC-DepthV3, a self-supervised monocular depth estimation method that integrates pseudo-depth priors and enhanced loss functions to improve robustness and accuracy in dynamic scenes. Their results showed that this approach outperforms existing baselines across multiple public datasets. Additionally, [PERSON] et al. [28] presented MultiSiam, a self-supervised multi-instance Siamese representation learning framework tailored for autonomous driving. This method incorporates IoU-based positive sample definition, feature alignment, and intra-image clustering with self-attention to boost generalization and downstream performance. Their approach surpasses methods such as MoCo and BYOL on the Cityscapes and BDD100K datasets. To address the aforementioned challenges, we propose a self-supervised representation learning framework based on multi-view consistency constraints. The proposed framework aims to model latent structure using unlabeled data, preserve the structural information of tabular data, and achieve both feature completion and semantic enhancement. Unlike traditional supervised learning approaches, our proposed method does not rely on full supervision; instead, useful information is extracted from unlabeled samples and latent patterns in the data are fully exploited through a self-supervised learning paradigm. The contributions of the proposed method are summarized as follows:  
1. A multi-view consistency constraint mechanism is designed to encode data through multiple views, enabling the model to learn stable and discriminative latent representations from distinct feature subspaces. Consistency-based contrastive learning is employed to enhance the ability to capture relationships across views, improving both robustness and generalization. 2. A view-disentangled encoder is developed in order to effectively address the heterogeneity of tabular data. Data features are partitioned into multiple subspaces, each corresponding to a distinct view, with independently designed encoding paths for each view. This design allows the model to capture localized structure and improves its representational capacity. 3. A cross-view consistency loss is introduced during training to ensure alignment of latent representations across different views. Such a constraint guides the model to learn semantically meaningful features, thereby enhancing its performance on diverse datasets. 4. A self-supervised learning framework is adopted, allowing the proposed model to leverage the latent information in unlabeled data. This approach addresses the challenges of label sparsity and missing values, enabling effective training even under incomplete data conditions. The remainder of this paper is structured as follows: Section 2 reviews related work on sparse structured data modeling, self-supervised learning, and multi-view consistency modeling; Section 3 details the datasets, preprocessing and augmentation strategies, and overall architecture and core components of the proposed self-supervised learning framework with multi-view consistency constraints; Section 4 presents the complete experimental results and analysis, including comparisons with various baseline models, module-level ablation studies, and parameter sensitivity evaluations, along with an in-depth discussion of the method's limitations and potential directions for future research; finally, Section 5 concludes the paper by summarizing the key contributions of this study.