Gaussian Processes for Missing Value Imputation
Missing values are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference scale to large data sets. Sparse GPs can be used to compute a predictive distribution for missing data. Here, we present a hierarchical composition of sparse GPs that is used to predict missing values at each dimension using all the variables from the other dimensions. We call the approach missing GP (MGP). MGP can be trained simultaneously to impute all observed missing values. Specifically, it outputs a predictive distribution for each missing value that is then used in the imputation of other missing values. We evaluate MGP in one private clinical data set and four UCI datasets with a different percentage of missing values. We compare the performance of MGP with other state-of-the-art methods for imputing missing values, including variants based on sparse GPs and deep GPs. The results obtained show a significantly better performance of MGP.
Code (1)
Tasks
Gaussian ProcessesImputationMissing ValuesVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes
Healthcare data, particularly in critical care settings, presents three key challenges for analysis. First, physiological measurements come from different sources but are inherently related. Yet, traditional methods ofte…
Gaussian ProcessesImputationMissing ValuesUncertainty QuantificationOnline Missing Value Imputation and Change Point Detection with the Gaussian Copula
Missing value imputation is crucial for real-world data science workflows. Imputation is harder in the online setting, as it requires the imputation method itself to be able to evolve over time. For practical application…
Change Point DetectionImputationMissing ValuesMixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
The problem of missing values in multivariable time series is a key challenge in many applications such as clinical data mining. Although many imputation methods show their effectiveness in many applications, few of them…
Gaussian ProcessesImputationMissing ValuesTime Series+1Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Modern large scale datasets are often plagued with missing entries. For tabular data with missing values, a flurry of imputation algorithms solve for a complete matrix which minimizes some penalized reconstruction error.…
ImputationMatrix CompletionMissing ValuesRecommendation SystemsHandling missing data in model-based clustering
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missing values can largely impact the GMMs est…
ClusteringData AugmentationDensity EstimationGeneral Classification+3