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Data imputation by pursuing better classification: A supervised kernel-based method
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DOI：10.1016/j.patcog.2025.112312.png)
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Abstract
En 中文
• We propose a novel two-stage data imputation framework. • We optimize the similarity relationships between data by utilizing supervision information and guide the data imputation process. • We develop a nonparametric method to impute the kernel matrix, which is performed alternately with the training of the classifier. • We provide a solving algorithm based on the block coordinate descent to accurately recover missing data from a given kernel matrix. • When the missing ratio exceeds 60 %, our algorithm outperforms other imputation methods significantly.
Keywords:
data imputation
kernel matrix
nonparametric method
block coordinate descent
similarity optimization
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
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