Results 21 to 30 of about 3,622,541 (329)
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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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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High-Dimensional Matched Subspace Detection When Data are Missing [PDF]
, 2010 We consider the problem of deciding whether a highly incomplete signal lies within a given subspace. This problem, Matched Subspace Detection, is a classical, well-studied problem when the signal is completely observed. High- dimensional testing problems
Balzano, Laura +2 more
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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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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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Robust nearest-neighbor methods for classifying high-dimensional data [PDF]
, 2009 We suggest a robust nearest-neighbor approach to classifying high-dimensional data. The method enhances sensitivity by employing a threshold and truncates to a sequence of zeros and ones in order to reduce the deleterious impact of heavy-tailed data ...
Chan, Yao-ban, Hall, Peter
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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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Program Evaluation and Causal Inference with High-Dimensional Data [PDF]
, 2016 In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables,
Belloni, Alexandre +3 more
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