# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 4 Results and Discussion
### Discussion  
A series of experiments was conducted to address the challenges posed by label sparsity and feature incompleteness in structured data. These experiments demonstrated the robustness and adaptability of the proposed multi-view consistency-based SSL framework across multiple real-world application scenarios. The experimental design encompassed evaluations of different view partitioning strategies, configurations of contrastive loss components, and the sensitivity of model performance to the number of views. Across various datasets, strong generalization capabilities were consistently observed, particularly in tasks such as user behavior analysis, financial risk prediction, and operational log modeling. For instance, user interactions with items are typically extremely sparse in user rating scenarios such as those encountered on e-commerce or video streaming platforms, with most users providing feedback on only a limited number of items. Traditional models such as logistic regression or XGBoost rely heavily on abundant and high-quality labels to accurately model user preferences, resulting in poor performance in sparse rating contexts. By incorporating a multi-view partitioning mechanism, the proposed method encodes user features, item attributes, and behavioral context into distinct views. Masked prediction and consistency-based contrastive objectives are then applied to recover stable latent representations from incomplete data. Even with limited rating information, this strategy enables the extraction of discriminative preference representations by modeling inter-feature relationships. As a result, the proposed method significantly outperforms baselines in terms of both accuracy and AUC. In the context of financial transaction data such as credit scoring or fraud detection systems, models must often contend with numerous missing or inconsistent fields, for example in cases where users omit employment details, transaction types are anonymized, or default records are incomplete. This issue is especially prominent in micro-enterprise lending, where submitted information is typically sparse and label availability is minimal. In scenarios with only 10% to 20% labeled samples, the performance of traditional supervised methods in identifying high-risk users deteriorates markedly. In contrast, the  
Figure 7: Accuracy Progression Across Different Number of Views (K). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Number of Views (\(k\))** & **Accuracy** & **F1-Score** & **AUC** & **Normalized MSE** \\ \hline
8 & 0.68 & 0.63 & 0.70 & 0.185 \\
2 & 0.70 & 0.66 & 0.72 & 0.172 \\
6 & 0.77 & 0.72 & 0.80 & 0.120 \\
4 & 0.87 & 0.83 & 0.90 & 0.072 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of different view counts (\(k\)) under missing labels and incomplete features. proposed framework exhibits substantial robustness, which can be attributed to its use of consistency constraints to align latent variables across views. This facilitates the extraction of stable structural representations from sparsely populated feature spaces, resulting in high predictive accuracy for critical metrics such as default probability and credit rating. Further analysis of the contrastive loss design revealed that using either intra-view or cross-view consistency alone offers limited performance gains. However, their combination consistently yields superior stability and accuracy. In the case of social media log data, user behaviors observed across different pages can differ due to device type, network conditions, or time of interaction, introducing inter-view asymmetry. Intra-view contrast alone fails to reconcile such disparities, while cross-view contrast is susceptible to local perturbations. Combining both mechanisms enhances the model's adaptability to structural variations and stabilizes behavioral representations, enabling more accurate inference of user intent and motivation. This makes the method particularly suitable for intelligent recommendation and interest modeling systems.