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Clustering With Outlier Removal [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters.
Jun Li, Hongfu Liu, Yun Fu
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An Adaptive Outlier Removal Aided k-Means Clustering Algorithm
Journal of King Saud University - Computer and Information Sciences, 2022 K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of
Nor Ashidi Mat Isa +1 more
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Improving K-Means by Outlier Removal [PDF]
Lecture Notes in Computer Science, 2005 We present an Outlier Removal Clustering (ORC) algorithm that provides outlier detection and data clustering simultaneously. The method employs both clustering and outlier discovery to improve estimation of the centroids of the generative distribution. The proposed algorithm consists of two stages.
Tomi Kinnunen +2 more
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A Geometrical–Statistical Approach to Outlier Removal for TDOA Measurements [PDF]
IEEE Transactions on Signal Processing, 2017 30 pages, 10 figure, 3 tables, in press on IEEE Transactions on Signal ...
Alessia Pini +2 more
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Neural Network-Based Stereo Vision Outlier Removal [PDF]
MATEC Web of Conferences, 2022 Stereo vision systems rely on accurate feature matching to provide valid stereo reconstruction and pose estimation. This accuracy is achieved through outlier removal techniques, such as RANSAC. However, images also contain semantic information, which can
Strauss March, van Daalen Corné E.
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Improved Ellipse Fitting Algorithm with Outlier Removal [PDF]
Jisuanji kexue, 2022 The results of ellipse fitting can be considerably distorted by outliers in the fitted point set.To tackle this problem, three improved ellipse fitting algorithms, one of which is based on least trimmed square, and the other two on dual point removal ...
GUO Si-yu, WU Yan-dong
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CAISOV: Collinear Affine Invariance and Scale-Orientation Voting for Reliable Feature Matching
Remote Sensing, 2022 Reliable feature matching plays an important role in the fields of computer vision and photogrammetry. Due to the complex transformation model caused by photometric and geometric deformations, and the limited discriminative power of local feature ...
Haihan Luo +5 more
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Outlier removal using duality [PDF]
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010 In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step
Carl Olsson +2 more
openaire +5 more sources
STAR_outliers: a python package that separates univariate outliers from non-normal distributions
BioData Mining, 2023 There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers.
John T. Gregg, Jason H. Moore
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Nearest Centroid Classifier with Outlier Removal for Classification
JITeCS (Journal of Information Technology and Computer Science), 2020 Classification method is misled by outlier. However, there are few research of classification with outlier removal, especially for Nearest Centroid Classifier Method. The proposed methodology consists of two stages.
Aditya Hari Bawono +2 more
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