# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 4 Results and Discussion
### Computational Complexity Analysis  
To assess the computational efficiency of the implemented methods, both theoretical and empirical comparisons were conducted. The dominant time complexity of each model was analyzed with respect to the input size \(n\) (number of samples) and feature dimension \(d\). Shallow models such as logistic regression exhibit linear time complexity \(\mathcal{O}(nd)\), as they primarily involve matrix-vector multiplications. Gradient-boosted trees (e.g., XGBoost) typically require \(\mathcal{O}(nd\log n)\) due to tree construction over multiple boosting rounds. VIME adds an auxiliary encoder and feature masking, resulting in an approximate complexity of \(\mathcal{O}(nd^{2})\) per batch. TabNet incorporates sequential attention steps with sparse feature selection, yielding an overall complexity between \(\mathcal{O}(nd)\) and \(\mathcal{O}(nd^{2})\). Transformer-based architectures such as TabTransformer and BERT rely on self-attention operations with complexity \(\mathcal{O}(nd^{2}+d^{3})\). Our proposed method introduces multi-view encoding and inter-view attention, leading to a complexity of \(\mathcal{O}(nkd^{2})\), where \(k\) is the number of views. Despite the higher theoretical cost, it is designed to support efficient parallel computation. The practical computational cost was also measured in terms of training time per epoch, inference latency per sample, and peak GPU memory usage. All experiments were conducted under identical hardware settings. The results were averaged over five Monte Carlo runs and are presented in Table 6.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Training Time (s/Epoch)** & **Inference Time (ms/Sample)** & **Peak Memory (MB)** \\ \hline Logistic Regression & 0.2 & 0.03 & 50 \\ XGBoost & 1.5 & 0.05 & 130 \\ TabNet & 3.2 & 0.08 & 280 \\ TabTransformer & 7.8 & 0.19 & 820 \\ BERT & 16.4 & 0.29 & 1024 \\ VIME & 4.2 & 0.09 & 310 \\ Proposed Method & 12.7 & 0.25 & 940 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational cost comparison across models.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **F1-Score** & **AUC** & **Normalized MSE** \\ \hline Logistic Regression & 0.690 \(\pm\) 0.008 & 0.650 \(\pm\) 0.011 & 0.710 \(\pm\) 0.009 & 0.164 \(\pm\) 0.012 \\ XGBoost & 0.730 \(\pm\) 0.006 & 0.690 \(\pm\) 0.007 & 0.760 \(\pm\) 0.005 & 0.147 \(\pm\) 0.008 \\ TabNet & 0.750 \(\pm\) 0.006 & 0.700 \(\pm\) 0.007 & 0.780 \(\pm\) 0.006 & 0.130 \(\pm\) 0.007 \\ TabTransformer & 0.780 \(\pm\) 0.004 & 0.730 \(\pm\) 0.005 & 0.800 \(\pm\) 0.003 & 0.122 \(\pm\) 0.006 \\ VIME & 0.800 \(\pm\) 0.007 & 0.760 \(\pm\) 0.008 & 0.840 \(\pm\) 0.005 & 0.108 \(\pm\) 0.009 \\ BERT & 0.830 \(\pm\) 0.004 & 0.790 \(\pm\) 0.006 & 0.860 \(\pm\) 0.003 & 0.089 \(\pm\) 0.005 \\ Proposed Method & **0.870 \(\pm\) 0.003 \({}^{\star}\)** & **0.830 \(\pm\) 0.004 \({}^{\star}\)** & **0.900 \(\pm\) 0.003 \({}^{\star}\)** & **0.066 \(\pm\) 0.004 \({}^{\star}\)** \\ \hline \hline \end{tabular}  
* \(p\)-value & **0.031** & **0.028** & **0.019** & **0.015** \\ \hline \hline \end{tabular}