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Outlier detection using isolation forest and local outlier factor
Proceedings of the Conference on 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. However, iForest is only sensitive to global outliers, and is weak in dealing with local outliers. Although LOF performs well
Zhangyu Cheng +2 more
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Robust feature matching using guided local outlier factor
Pattern Recognition, 2021Abstract Matching local features on two or more images is fundamental for many applications in the field of computer vision and pattern recognition. Identifying and rejecting mismatches is an important part in the framework of feature matching, due to the putative correspondences always contaminated by mismatches with the error-prone local feature ...
Gang Wang 0008, Yufei Chen 0002
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Local peculiarity factor and its application in outlier detection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008Peculiarity oriented mining (POM), aiming to discover peculiarity rules hidden in a dataset, is a new data mining method. In the past few years, many results and applications on POM have been reported. However, there is still a lack of theoretical analysis.
Jian Yang 0016 +3 more
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Batch Process Modeling and Monitoring With Local Outlier Factor
IEEE Transactions on Control Systems Technology, 2019Batch processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, batch-to-batch processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for ...
Jinlin Zhu +3 more
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Weighted Local Outlier Factor for Detecting Anomaly on In-Vehicle Network
2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020Modern vehicles are generally equipped with dozens of (or even hundreds of) electronic and intelligent devices and bloom into more involved information hub in enabling V2X networking. Protecting this increasingly complex vehicle ecosystem can be an arduous task, especially as the proliferation of data across distinct connected devices makes them more ...
Yuan Linghu +3 more
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2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 2015
Outlier detection is a basic task in network analysis, which is useful in many applications such as intrusion detection, criminal investigation, and information filtering. In this paper we proposed a hybrid outlier detection methods in complex networks based on Vertex Distributed Representation and Local Outlier Factor, with the aim to find abnormal ...
Zili Li 0005, Li Zeng
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Outlier detection is a basic task in network analysis, which is useful in many applications such as intrusion detection, criminal investigation, and information filtering. In this paper we proposed a hybrid outlier detection methods in complex networks based on Vertex Distributed Representation and Local Outlier Factor, with the aim to find abnormal ...
Zili Li 0005, Li Zeng
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Adaptive fuzzy C-means clustering integrated with local outlier factor
Intelligent Data Analysis, 2022The conventional fuzzy C-means (FCM) is sensitive to the initial cluster centers and outliers, which may cause the centers deviate from the real centers when the algorithm converges. To improve the performance of FCM, a method of initializing the cluster centers based on probabilistic suppression is proposed and an improved local outlier factor is ...
Chunyan She +4 more
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An Efficient Switching Median Filter Based on Local Outlier Factor
IEEE Signal Processing Letters, 2011An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND).
Wei Wang, Peizhong Lu
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Instance Weighted Clustering: Local Outlier Factor and K-Means
2020Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and ...
Paul Moggridge +4 more
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