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Barekeng, 2022 The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However,
Anwar Fitrianto, Sim Hui Xin
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On-line detection algorithm of ore grade change in grinding grading system
Open Physics, 2020 In process industry control, process data is critical for both control and fault diagnosis. Timely detection of outliers and mutation point in process data can quickly adjust control parameters or discover potential failures throughout the system. Aiming
Zhao Jianjun, Zhou Junwu
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Outlier Edge Detection Using Random Graph Generation Models and Applications [PDF]
, 2016 Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users ...
A Lancichinetti +30 more
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A GMS-Guided Approach for 2D Feature Correspondence Selection
IEEE Access, 2020 Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision.
Qunfang Tang +5 more
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Outlier Elimination for Robust Ellipse and Ellipsoid Fitting
, 2009 In this paper, an outlier elimination algorithm for ellipse/ellipsoid fitting is proposed. This two-stage algorithm employs a proximity-based outlier detection algorithm (using the graph Laplacian), followed by a model-based outlier detection algorithm ...
Kulkarni, Sanjeev R. +3 more
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Universal Outlier Hypothesis Testing
, 2014 Outlier hypothesis testing is studied in a universal setting. Multiple sequences of observations are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are distributed according to an
Li, Yun +2 more
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HOS-Miner: a system for detecting outlying subspaces of high-dimensional data [PDF]
, 2004 [Abstract]: We identify a new and interesting high-dimensional outlier detection problem in this paper that is, detecting the subspaces in which given data points are outliers.
Ling, Tok Wang +3 more
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Journal of Techniques, 2021 The occurrence of tremendous developments in the field of data has led to the formation of huge volumes of data, and it is normal that this leads to the presence of outliers in this data for many reasons, which may have small or large values compared ...
Emad Obaid Merza, Nashaat Jasim Mohammed
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Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection
Cauchy: Jurnal Matematika Murni dan AplikasiOutliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research.
Annisa Putri Utami +2 more
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Anomaly-Tolerant Traffic Matrix Estimation via Prior Information Guided Matrix Completion
IEEE Access, 2017 Network management, planning, and optimization rely on accurate and complete traffic measurement. However, anomalies and missing data are inevitable in direct traffic measurement because of high measurement costs and unreliable network transport ...
Wencai Ye +4 more
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