Results 41 to 50 of about 4,092,880 (310)
Nonparametric Tests Applicable to High Dimensional Data
Austrian Journal of Statistics, 2019 Constructions of data driven ordering of set of multivariate observations are presented. The methods employ also dissimilarity measures. The ranks are used in the construction of test statistics for location problem and in the construction of the ...
Frantisek Rublik
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Missing Data Imputation with High-Dimensional Data
The American Statistician, 2023 Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables.
Alberto Brini, Edwin R. van den Heuvel
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High Dimensional Data Clustering using Self-Organized Map
Knowledge Engineering and Data Science, 2019 As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data
Ruth Ema Febrita +2 more
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Data for Ensemble Riemannian Data Assimilation for High-dimensional Nonlinear Dynamics
, 2021 Tamang, Sagar, K; Ebtehaj, Ardeshir; van Leeuwen, Peter, J; Lerman, Gilad; Foufoula-Georgiou, Efi. (2021). Data for Ensemble Riemannian Data Assimilation for High-dimensional Nonlinear Dynamics.
Ebtehaj, Ardeshir +4 more
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Frontiers in Cardiovascular Medicine, 2022 Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment.
Haidee Chen +5 more
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Recession forecasting with high‐dimensional data [PDF]
Journal of Forecasting, 2021 AbstractIn this paper, a large amount of different financial and macroeconomic variables are used to predict the U.S. recession periods. We propose a new cost‐sensitive extension to the gradient boosting model, which can take into account the class imbalance problem of the binary response variable.
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Finding k-Dominant G-Skyline Groups on High Dimensional Data
IEEE Access, 2018 Skyline query retrieves a set of skyline points which are not dominated by any other point and has attracted wide attention in database community. Recently, an important variant G-Skyline is developed. It aims to return optimal groups of points. However,
Kaiqi Zhang +3 more
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A classification method for high‐dimensional imbalanced multi‐classification data
Electronics Letters, 2023 High‐dimensional imbalanced multi‐classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification.
Mengmeng Li +5 more
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An efficient predictive analytics system for high dimensional big data
Journal of King Saud University: Computer and Information Sciences, 2022 The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data.
Myat Cho Mon Oo, Thandar Thein
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Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA
Frontiers in Neuroscience, 2022 High-dimensional biomedical data contained many irrelevant or weakly correlated features, which affected the efficiency of disease diagnosis. This manuscript presented a feature selection method for high-dimensional biomedical data based on the ...
Yongqiang Dai +3 more
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