Meta-GAIN for Missing Data Imputation

Although previous deep imputation methods (e.g., Generative Adversarial Network (GAN) based methods) have been widely designed to impute missing data, they still suffer from the issues, i.e., lack of the imputation diversity and the generalization ability. In this paper, we propose a new GAN-based imputation method, namely Meta-based Generative Adversarial Imputation Network (Meta-GAIN), to investigate a new generator for achieving diverse imputation and generalization ability. Specifically, we employ the Kullback-Leibler (KL) divergence to achieve the imputation diversity by generating a continuous embedding space of the original data. We also design a task regularizer to suppress redundant features and capture a more authentic distribution, thus enhancing the generalization ability of the imputation model. Moreover, we theoretically prove that our proposed regularizer achieves the generalization ability. In addition, we design a new meta network to efficient optimize our objective function as well as to improve imputation diversity. Experimental results on real datasets show that our method outperforms all comparison methods under different missing mechanisms in terms of imputation and classification performance.