Results 31 to 40 of about 343,541 (352)
Mean-shift outlier detection and filtering
Pattern Recognition, 2021 Traditional outlier detection methods create a model for data and then label as outliers for objects that deviate significantly from this model. However, when dat has many outliers, outliers also pollute the model.
Jiawei Yang, S. Rahardja, P. Fränti
semanticscholar +1 more source
Scheduling with Outliers [PDF]
, 2009 23 pages, 3 ...
Anupam Gupta 0001 +3 more
openaire +2 more sources
ℓ1 Major Component Detection and Analysis (ℓ1 MCDA) in Three and Higher Dimensional Spaces
Algorithms, 2014 Based on the recent development of two dimensional ℓ1 major component detection and analysis (ℓ1 MCDA), we develop a scalable ℓ1 MCDA in the n-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with ...
Zhibin Deng +3 more
doaj +1 more source
A Novel Outlier-Robust Kalman Filtering Framework Based on Statistical Similarity Measure
IEEE Transactions on Automatic Control, 2021 In this article, a statistical similarity measure is introduced to quantify the similarity between two random vectors. The measure is, then, employed to develop a novel outlier-robust Kalman filtering framework. The approximation errors and the stability
Yulong Huang +4 more
semanticscholar +1 more source
On replacement of outliers and missing values in time series
EQA, 2023 Presence of missing values and occurrence of outliers in time series cause many hindrances in the analysis of data. Several methods are proposed for determining estimates to replace the missing values and outliers.
Loganathan Appaia, Sumithra Palraj
doaj +1 more source
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection [PDF]
IEEE Robotics and Automation Letters, 2019 Semidefinite Programming (SDP) and Sums-of-Squ- ares (SOS) relaxations have led to certifiably optimal non-minimal solvers for several robotics and computer vision problems.
Heng Yang +3 more
semanticscholar +1 more source
Advancements of Outlier Detection: A Survey [PDF]
, 2013 Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements
Zhang, Ji, Ji Zhang
core +1 more source
Current Biology, 2013 SummaryReports of photographic memory are widespread but the search for truly extraordinary memory abilities has uncovered only a handful of examples. But the fact that such individuals exist has fascinated and begs the question, what is the source of such abilities? Cyrus Martin reports.
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A Survey of Outlier Detection Techniques in IoT: Review and Classification
J. Sens. Actuator Networks, 2022 The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station.
Mustafa Al Samara +3 more
semanticscholar +1 more source
A Sliding Window Variational Outlier-Robust Kalman Filter Based on Student’s t-Noise Modeling
IEEE Transactions on Aerospace and Electronic Systems, 2022 Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this article, the measurements in a sliding window are, therefore,
Fengchi Zhu +4 more
semanticscholar +1 more source