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Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding
Web Search and Data Mining, 2020Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks.
S. Bandyopadhyay +3 more
semanticscholar +1 more source
Hastings Center Report, 2013
AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
openaire +2 more sources
AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
openaire +2 more sources
ACM Computing Surveys, 2020
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments ...
A. Boukerche, Lining Zheng, O. Alfandi
semanticscholar +1 more source
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments ...
A. Boukerche, Lining Zheng, O. Alfandi
semanticscholar +1 more source
Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data
IEEE Transactions on Cybernetics, 2021Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data.
Zhong Yuan +4 more
semanticscholar +1 more source
Outlier detection using isolation forest and local outlier factor
Research in Adaptive and Convergent Systems, 2019Outlier detection, also named as anomaly detection, is one of the hot issues in the field of data mining. As well-known outlier detection algorithms, Isolation Forest(iForest) and Local Outlier Factor(LOF) have been widely used.
Zhangyu Cheng +2 more
semanticscholar +1 more source
Robust Regression and Outlier Detection
Wiley Series in Probability and Statistics, 2005P. Rousseeuw, A. Leroy
semanticscholar +1 more source
A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Angel Conde, Jose A A Lozano
exaly