Results 31 to 40 of about 3,622,541 (329)
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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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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Tests for covariance matrices, particularly for high dimensional data [PDF]
, 2010 Test statistics for sphericity and identity of the covariance matrix are presented, when the data are multivariate normal and the dimension, p, can be larger than the sample size, n.
Ahmad, M. Rauf
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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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Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data [PDF]
, 2015 Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research.
Liu, Lingqiao +4 more
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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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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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