Sparse Conditional Diffusion Alignment for Reconstruction and Statistical Alignment under Sparse Observations

Sparse and incomplete observations often pose significant challenges for generative models, especially in high-dimensional statistical applications. We propose SCDA (Sparse Conditional Diffusion Alignment), a novel framework that specifically addresses the limitations of existing conditional diffusion and sparse reconstruction methods by integrating sparsity-aware mechanisms with explicit statistical alignment constraints. Unlike prior approaches, SCDA jointly models conditional generation under sparse observations while enforcing distributional consistency. SCDA enables accurate reconstruction of missing or unobserved data while maintaining statistical consistency with the observed distribution. By incorporating adaptive sparsity masks and cross-distribution alignment, our method achieves robust generation under varying sparsity levels. Extensive experiments on publicly available real-world sparse datasets demonstrate that SCDA outperforms existing conditional diffusion approaches in both reconstruction fidelity and distributional alignment, providing a reliable tool for statistical inference and high-dimensional data analysis under sparse observations.