Results 31 to 40 of about 963,417 (295)
Improving K-Means by Outlier Removal [PDF]
, 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.
Ville Hautamäki +4 more
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Frontiers in Neuroscience, 2023 Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are ...
Qichao Han +6 more
doaj +1 more source
Robust principal component analysis for accurate outlier sample detection in RNA-Seq data
BMC Bioinformatics, 2020 Background High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical ...
Xiaoying Chen +4 more
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Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking
Sensors, 2023 Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife.
Eitam Arnon +5 more
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Outlier Removal in 2D Leap Frog Algorithm [PDF]
, 2012 In this paper a 2D Leap Frog Algorithm is applied to solve the so-called noisy Photometric Stereo problem. In 3-source Photometric Stereo (noiseless or noisy) an ideal unknown Lambertian surface is illuminated from distant light-source directions (their directions are assumed to be linearly independent).
Ryszard Kozera, Tchórzewski, Jacek
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A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data
IEEE Access, 2020 Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models ...
Kathleen Champion +4 more
doaj +1 more source
Outlier removal for table model.
, 2018 A: Table model with different outliers, B: isolated outlier removal, C: sparse outlier are removal, D: non-isolated outlier removal result.
Fan Li (49953) +3 more
core +1 more source
A Multi-Stage Outlier Removal Method for Point Clouds with High Outlier Ratio
Remote SensingPoint clouds acquired by LiDAR typically contain a large number of outliers, which can substantially degrade downstream processing. Existing outlier removal methods suffer from significant performance degradation on point clouds with high outlier ratios.
Zihan Meng +3 more
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Adaptive parameter local consistency automatic outlier removal algorithm for area-based matching [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesDue to the influence of image differences and matching methods, geometric calibration of remote sensing images often results in the extraction of control points with inevitable outliers.
T. Huang, H. Pan, N. Zhou
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Outlier removal for chair model.
, 2018 A: Chair model with different outliers, B: isolated outlier removal, C: sparse outlier removal, D: non-isolated outlier removal result.
Fan Li (49953) +3 more
core +1 more source