Semi-supervised learning with missing values imputation

Incomplete instances with various missing attributes in many real-world applications have brought challenges to the classification tasks. Unsupervised imputation is often employed to replace the missing values with substitute values before supervised classification. However, this process often separates the imputation and classification, which may lead to inferior performance since the separated two tasks ignore the data distribution and label information contained in each other. Besides, traditional methods may rely on improper assumptions to initialize the missing values, whereas the unreliability of such initialization might degrade the performance. To address these problems, a novel semi-supervised conditional normalizing flow (SSCFlow) is proposed in this paper. SSCFlow combines unsupervised imputation and supervised classification as a joint semi-supervised task, which estimates the conditional distribution of incomplete instances to facilitate the imputation and classification simultaneously. Moreover, SSCFlow treats the initialized missing values as corrupted initial imputations and iteratively reconstructs their latent representations to approximate their true conditional distribution. Experiments on real-world datasets demonstrate the robustness and effectiveness of the proposed algorithm.

Automated summary

This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.