Prediction of free air anomaly obtained by geophysical multivariate data in Amazonia: An approach to missing data imputation with Self-Organizing Maps

2017 ◽  
Author(s):  
Bruno Monserrat Perillo ◽  
Cleyton de Carvalho Carneiro ◽  
Ana Cristina de Matos ◽  
Denizar Blitzkow ◽  
Stephen Fraser
Keyword(s):  
Missing Data ◽  
Multivariate Data ◽  
Data Imputation ◽  
Self Organizing Maps ◽  
Missing Data Imputation ◽  
Free Air ◽  
Free Air Anomaly ◽  
Self Organizing

Missing data imputation in multivariate data by evolutionary algorithms

2011 ◽  
Vol 27 (5) ◽  
pp. 1468-1474 ◽  
Author(s):  
Juan C. Figueroa García ◽  
Dusko Kalenatic ◽  
Cesar Amilcar Lopez Bello
Keyword(s):  
Missing Data ◽  
Evolutionary Algorithms ◽  
Multivariate Data ◽  
Data Imputation ◽  
Missing Data Imputation

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

Missing data imputation in meteorological datasets with the GAIN method

2021 ◽  
Author(s):  
Marina Popolizio ◽  
Alberto Amato ◽  
Tiziano Politi ◽  
Roberto Calienno ◽  
Vincenzo Di Lecce
Keyword(s):  
Missing Data ◽  
Data Imputation ◽  
Missing Data Imputation
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