Semi-supervised Learning with Missing Values Imputation
This addresses the challenge of incomplete data in classification tasks, offering a more integrated approach compared to traditional methods, though it appears incremental as it builds on existing imputation and flow techniques.
The paper tackles the problem of classification with missing data by proposing a semi-supervised conditional normalizing flow (SSCFlow) that integrates imputation and classification using label information, resulting in improved performance on real-world datasets.
Incomplete instances with various missing attributes in many real-world applications have brought challenges to the classification tasks. Missing values imputation methods are often employed to replace the missing values with substitute values. However, this process often separates the imputation and classification, which may lead to inferior performance since label information are often ignored during imputation. Moreover, traditional methods may rely on improper assumptions to initialize the missing values, whereas the unreliability of such initialization might lead to inferior performance. To address these problems, a novel semi-supervised conditional normalizing flow (SSCFlow) is proposed in this paper. SSCFlow explicitly utilizes the label information to facilitate the imputation and classification simultaneously by estimating the conditional distribution of incomplete instances with a novel semi-supervised normalizing flow. Moreover, SSCFlow treats the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate their true conditional distribution. Experiments on real-world datasets demonstrate the robustness and effectiveness of the proposed algorithm.