Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery

arXiv:2607.06930v1 Announce Type: new Abstract: Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis. Real-world datasets often exhibit complex latent structures composed of multiple subgroups with distinct distributions. However, existing methods often overlook such population heterogeneity. Without explicit structural guidance, these methods tend to produce generic estimates that blur subgroup boundaries and lack instance-level fidelity. While incorporating subgroup information offers a remedy, it faces a cir

Source: arXiv cs.LG — read the full report at the original publisher.

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