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Data integration with high dimensionality
Biometrika, 2017 SummaryWe consider situations where the data consist of a number of responses for each individual, which may include a mix of discrete and continuous variables. The data also include a class of predictors, where the same predictor may have different physical measurements across different experiments depending on how the predictor is measured.
Gao, Xin, Carroll, Raymond J.
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Testing the additional predictive value of high-dimensional molecular data [PDF]
, 2009 While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data ...
Torsten Hothorn +5 more
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Bootstrap in high dimensional spaces [PDF]
, 2020 Ziel dieser Arbeit ist theoretische Eigenschaften verschiedener Bootstrap Methoden zu untersuchen. Als Ergebnis führen wir die Konvergenzraten des Bootstrap-Verfahrens ein, die sich auf die Differenz zwischen der tatsächlichen Verteilung einer Statistik ...
Buzun, Nazar
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Attention-driven tree-structured convolutional LSTM for high dimensional data understanding
Frontiers in Physics, 2023 Modeling sequential information for image sequences is a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems.
Yi Lu +8 more
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Knowledge Transfer Between Artificial Intelligence Systems
Frontiers in Neurorobotics, 2018 We consider the fundamental question: how a legacy “student” Artificial Intelligent (AI) system could learn from a legacy “teacher” AI system or a human expert without re-training and, most importantly, without requiring significant computational ...
Ivan Y. Tyukin +6 more
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Sparse sliced inverse regression for high dimensional data analysis
BMC Bioinformatics, 2022 Background Dimension reduction and variable selection play a critical role in the analysis of contemporary high-dimensional data. The semi-parametric multi-index model often serves as a reasonable model for analysis of such high-dimensional data.
Haileab Hilafu, Sandra E. Safo
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, 2022 When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality.
Lingling Li +15 more
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HIGH-DIMENSIONAL DATA ANALYSIS [PDF]
, 2010 High-Dimensional Classification: High-Dimensional Classification (J-Q Fan et al.) Flexible Large Margin Classifiers (Y-F Liu & Y-C Wu) Large-Scale Multiple Testing: Large-Scale Multiple Testing (T T Cai & W-G Sun) Model Building with Variable Selection: Model Building with Variable Selection (M Yuan) Bayesian Variable Selection in Regression with ...
Tony Cai, Xiaotong Shen
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The properties of nonuniformity analysis of high dimensional data
Lietuvos Matematikos Rinkinys, 2004 A novel approach to outlier detection and clustering on the ground of the distribution of distances between multidimensional points is presented. The basic idea is to eval uate the outlier factor for each data point.
Vydūnas Šaltenis
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RLE plots: Visualizing unwanted variation in high dimensional data. [PDF]
PLoS ONE, 2018 Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data.
Luke C Gandolfo, Terence P Speed
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