Results 231 to 240 of about 225,245 (281)

Robust feature matching using guided local outlier factor

Pattern Recognition, 2021
Abstract 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, Yufei Chen
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Outlier detection using isolation forest and local outlier factor

Proceedings of the Conference on Research in Adaptive and Convergent Systems, 2019
Outlier 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, Chengming Zou, Jianwei Dong   +2 more
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Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

2020 39th Chinese Control Conference (CCC), 2020
In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex.
Yutang Xiao, Yang Tao, Hongbo Shi
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Dynamic process monitoring using adaptive local outlier factor

Chemometrics and Intelligent Laboratory Systems, 2013
Abstract A numerically efficient moving window local outlier factor (LOF) algorithm is proposed in this paper for monitoring industrial processes with time-varying and multimode characteristics. The key feature of the proposed algorithm can be identified as its underlying capability to handle complex data distributions and incursive operating ...
Yuxin Ma, Hongbo Shi, Hehe Ma, Mengling Wang   +3 more
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A Hybrid Vertex Outlier Detection Method Based on Distributed Representation and Local Outlier Factor

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, Li Zeng
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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, 2008
Peculiarity 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, Ning Zhong, Yiyu Yao, Jue Wang   +3 more
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Adaptive fuzzy C-means clustering integrated with local outlier factor

Intelligent Data Analysis, 2022
The 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 ...
She, Chunyan, Zeng, Shaohua, Wang, Qi, Wang, Shuai, Xu, Yidan   +4 more
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Instance Weighted Clustering: Local Outlier Factor and K-Means

2020
Clustering 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, Na Helian, Yi Sun, Mariana Lilley, Vito Veneziano   +4 more
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Self-Adaptive Negative Selection Using Local Outlier Factor

2012
Negative selection algorithm (NSA) classifies a given data either as normal (self) or anomalous (non-self). To make this classification, it is trained using normal (self) samples. NSA generates detectors to cover the complementary space of self in training phase. The classification of NSAs is mainly specified by two issues, self space determination and
Zafer Ataser, Ferda N. Alpaslan
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