Results 21 to 30 of about 1,150,244 (261)
Interpretable Approximation of High-Dimensional Data
SIAM Journal on Mathematics of Data Science, 2021 In this paper we apply the previously introduced approximation method based on the ANOVA (analysis of variance) decomposition and Grouped Transformations to synthetic and real data. The advantage of this method is the interpretability of the approximation, i.e., the ability to rank the importance of the attribute interactions or the variable couplings.
Daniel Potts, Michael Schmischke
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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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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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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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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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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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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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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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