Alternating clustering and GAN-based imputation in a feedback loop yields more accurate missing-value recovery on heterogeneous data than single-distribution methods.
Foundations and Trends®in Machine Learning11(5-6), 355–607 (2019)
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Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery
Alternating clustering and GAN-based imputation in a feedback loop yields more accurate missing-value recovery on heterogeneous data than single-distribution methods.