Missing Data Imputation under Manifold Hypothesis
The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold. Recent advances in mixture variational autoencoders (VAEs) provide a powerful tool for extracting such underlying structure in a faithful manner. The resulting geometric structure naturally introduces local and global relationships among variables, thereby providing a systematic way of imputing missing data. We propose a model-based imputation method that enables sampling from \( p(\bm{x}_{\mathrm{mis}} \mid \bm{x}_{\mathrm{obs}}) \) via a sampling-importance-resampling (SIR) procedure, which can be further augmented with a joint diffusion model in the latent space. Our met...
| Authors | Zelong Bi, Amuchechukwu Ibenegbu, Sarat Moka |
|---|---|
| Primary category | stat.ML |
| Published | 3 July 2026 |
| Last updated at source | 14 September 2026 |
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