# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method
### Data Augmentation  
Recent theoretical advances have emphasized that increasing the number of effective training samples through either data collection or data augmentation plays a critical role in reducing generalization error. The proxy learning curve of multiclass Bayesian classifiers demonstrates that, irrespective of the underlying model parameters, the mean squared error between the estimated and true posterior statistics decreases as the training set size \(N\) increases [51]. This convergence implies that augmenting training data via techniques such as imputation can bring the empirical risk closer to the Bayes risk. Moreover, for sparse structured datasets, the presence of missing values not only hinders training efficiency but also degrades predictive accuracy. Consequently, effective imputation of missing values during the preprocessing stage is essential for enhancing model generalization performance and preserving the integrity and consistency of the data. In the preprocessing stage, handling missing values is a critical task in structured data modeling. For sparse structured datasets, the presence of missing values not only affects training efficiency but also degrades prediction accuracy. Therefore, effective imputation of missing values is essential to maintain data integrity and consistency. Common approaches include statistical imputation and local distribution smoothing, both of which rely on analyzing the distribution patterns of observed data to reasonably infer the missing entries. Statistical imputation methods fill in missing values based on statistical properties of the data. Typical techniques include mean imputation, linear interpolation, and regression-based estimation. For continuous variables, mean imputation is the simplest approach. Missing values of a feature \(x_{i}\) are replaced with the mean of its observed values across the sample set. Let \(n\) be the total number of observations and let \(m\) be the number of missing entries; then, the imputed value is calculated as follows:  
\[\hat{x}_{i}=\frac{1}{n-m}\sum_{j\in D}x_{i,j} \tag{1}\]  
where \(\hat{x}_{i}\) denotes the imputed value and \(D\) represents the index set of non-missing values. In contrast, linear interpolation estimates missing values based on adjacent data points, while regression-based methods build predictive models to estimate the missing entries. For high \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data Type** & **Source** & **Sample Size** & **Feature Dimension** & **Max Missing Rate** \\ \hline User Rating Behavior & MovieLens, Book-Crossing & 170,000 & 50 & 28\% \\ Platform Activity Logs & Kaggle Web Logs & 200,000 & 60 & 33\% \\ Synthetic Sparse Data & Scikit-learn (Synthetic) & 100,000 & 100–200 & 40\% \\ Financial Transactions & IEEE-CIS, UCI Default & 180,000 & 120 & 35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structured data statistics used in the experiments. \begin{table}
\begin{tabular}{l c} \hline \hline
**Data Type** & **Class Type (Label Categories)** \\ \hline User Rating Behavior & Binary: Liked (rating \(\geq\) 4), Not Liked (rating \(<\) 4) \\ Platform Activity Logs & Binary: High-Intent Action, Low-Intent Action \\ Synthetic Sparse Data & Multi-class: 5 synthetic categories \\ Financial Transactions & Binary: Fraudulent, Non-Fraudulent \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Label categories used in the experiments. -dimensional data, mean or linear interpolation may overlook feature correlations, resulting in structurally inconsistent imputations. To address this, local distribution smoothing has been proposed. This method assumes local structural consistency and fills in missing values using weighted averages from neighboring data points.