Missing Data Imputation by Reducing Mutual Information with Rectified Flows
This paper introduces a novel iterative method for missing data imputation that sequentially reduces the mutual information between data and their corresponding missing mask. Inspired by GAN-based approaches, which train generators to decrease the predictability of missingness patterns, our method explicitly targets the reduction of mutual information. Specifically, our algorithm iteratively minimizes the KL divergence between the joint distribution of the imputed data and missing mask, and the product of their marginals from the previous iteration. We show that the optimal imputation under this framework corresponds to solving an ODE, whose velocity field minimizes a rectified flow training objective. We further illustrate that some existing imputation techniques can be interpreted as approximate special cases of our mutual-information-reducing framework. Comprehensive experiments on synthetic and real-world datasets validate the efficacy of our proposed approach, demonstrating superior imputation performance.
Code (0)
등록된 구현이 없습니다.
Tasks
ImputationSimilar Papers 제목 키워드 기반
Missing Data Imputation for Classification Problems
Imputation of missing data is a common application in various classification problems where the feature training matrix has missingness. A widely used solution to this imputation problem is based on the lazy learning tec…
ClassificationGeneral ClassificationImputationBoundary-enhanced time series data imputation with long-term dependency diffusion models
Data imputation is crucial for addressing challenges posed by missing values in multivariate time series data across various fields, such as healthcare, traffic, and economics, and has garnered significant attention. Amo…
ImputationMissing ValuesTime SeriesImputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series
Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantly focus on leveraging intra-series infor…
ImputationMemorizationMissing ValuesTime SeriesGlocal Information Bottleneck for Time Series Imputation
Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-world scenarios. Existing models typically o…
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features…