Results 51 to 60 of about 526,550 (191)
Comparative study on multivariate outlier detection methods in sesame (Sesamum indicum L.)
Electronic Journal of Plant Breeding, 2019 Outlier detection in multivariate dataset is not quite trivial when compared to univariate. The tediousness in multivariate outlier is due to presence of swamping and masking effect which portrays an ideal sample point as outlier instead of true one ...
K. Muthu Prabakaran +3 more
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Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies
NeuroImage, 2023 Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and
Viljami Sairanen, Jesper Andersson
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Provable Self-Representation Based Outlier Detection in a Union of Subspaces
, 2017 Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on
Robinson, Daniel P. +2 more
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Abnormal Data Detection Method Based on Ant Colony Algorithm [PDF]
Jisuanji gongcheng, 2016 Since the traditional outlier detection technology based on Omeasure needs to search all paths while detecting abnormal data and it is easy to make misjudgments under the scenario of less amount of data.Hence,it has obvious defects on the efficiency and
CAI Mei,LIU Bo
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A Comparison of Outlier Detection Techniques for High-Dimensional Data
International Journal of Computational Intelligence Systems, 2018 Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly.
Xiaodan Xu +3 more
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Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps
Big Data and Cognitive Computing, 2020 Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection.
Menglu Li, Rasha Kashef, Ahmed Ibrahim
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Frontiers in Astronomy and Space Sciences, 2022 Automatic detection of outliers is universally needed when working with scientific datasets, e.g., for cleaning datasets or flagging novel samples to guide instrument acquisition or scientific analysis.
Hannah R. Kerner +8 more
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Identification of Outlying Observations with Quantile Regression for Censored Data [PDF]
, 2014 Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results.
Cho, HyungJun +2 more
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Outlier Detection and Explanation Method Based on FOLOF Algorithm
EntropyOutlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to ...
Lei Bai, Jiasheng Wang, Yu Zhou
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Outliers in multivariate time series [PDF]
, 1998 This paper considers outliers in multivariate time series analysis. It generalizes four types of disturbances commonly used in the univariate time series analysis to the multivariate case, and investigates dynamic effects of a multivariate outlier on ...
Pankratz, Alan E. +2 more
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