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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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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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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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Entropy, 2022 Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).
Evgeny M. Mirkes +5 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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Journal of the National Science Foundation of Sri Lanka, 2016 This paper provides a brief introduction to high-dimensional data, a form of ‘Big Data’, and gives an overview of several data analysis concepts and techniques that could be used to explore and analyse such data. An example that involves genomics data from several Sri Lankan and United Kingdom oral cancer patients is used to illustrate the methods.
Dhammika Amaratunga, Javier Cabrera
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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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