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Cross-Outlier Detection [chapter]

Spiros Papadimitriou, Christos Faloutsos
2003 Lecture Notes in Computer Science  
Many of the existing outlier detection approaches cannot be extended to this case.  ...  The problem of outlier detection has been studied in the context of several domains and has received attention from the database research community.  ...  In brief, we argue that the following outlier detection problem is of practical importance: Problem 2 (Cross-outlier detection).  ... 
doi:10.1007/978-3-540-45072-6_12 fatcat:m2hoisnn4fbcxhbzipmmjbupri

Detecting Errors in Numerical Linked Data Using Cross-Checked Outlier Detection [chapter]

Daniel Fleischhacker, Heiko Paulheim, Volha Bryl, Johanna Völker, Christian Bizer
2014 Lecture Notes in Computer Science  
In this work, we instead propose an approach which combines the outcomes of two independent outlier detection runs to get a more reliable result and to also prevent problems arising from natural outliers  ...  Outlier detection used for identifying wrong values in data is typically applied to single datasets to search them for values of unexpected behavior.  ...  To prevent this false detection, we apply an additional cross-checking step to the results of the first outlier detection.  ... 
doi:10.1007/978-3-319-11964-9_23 fatcat:wtbqiki75nexzh6435mvqvz6wm

Supporting the search for cross-context links by outlier detection methods

Borut Sluban, Nada Lavrač
2010 BMC Bioinformatics  
Our work focuses on using noise/outlier detection methods for a novel task of cross-context link discovery.  ...  These outlier detection methods work in a 10-fold cross-validation manner, where repeatedly nine folds are used for training the classifier and on the complementary fold the misclassified instances are  ...  Our work focuses on using noise/outlier detection methods for a novel task of cross-context link discovery.  ... 
doi:10.1186/1471-2105-11-s5-p2 pmcid:PMC2956395 fatcat:svqswtm2gfc6lczutsll3f2dy4

Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining

I. Petric, B. Cestnik, N. Lavrac, T. Urbancic
2010 Computer journal  
The proposed approach contributes to cross-context link discovery by proving the utility of outlier detection for finding bisociative links in the process of autism literature exploration, as well as by  ...  It shows that detecting interesting outliers which appear in the literature on a given phenomenon can help the expert to find implicit relationships among concepts of different domains.  ...  Detection in Cross-Context Link Discovery for Creative Literature Mining at Jozef Stefan Institute on March 25, 2013 http://comjnl.oxfordjournals.org/ Downloaded from Outlier Detection in Cross-Context  ... 
doi:10.1093/comjnl/bxq074 fatcat:tkfrx66xenbxfpupkcq2i75b6q

Improvement of Serial Approach to Anomalous Sound Detection by Incorporating Two Binary Cross-Entropies for Outlier Exposure [article]

Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda, Tomoki Toda
2022 arXiv   pre-print
Anomalous sound detection systems must detect unknown, atypical sounds using only normal audio data.  ...  To explicitly distinguish these data, the proposed method uses multi-task learning of two binary cross-entropies when training OE.  ...  Currently, two types of ASD approaches are mainly used: inlier modeling (IM) and outlier exposure (OE).  ... 
arXiv:2206.05929v1 fatcat:w3xnmxpsgvb7zjtbhppx7a6uoy

An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset

Hui Lu, Yaxian Liu, Zongming Fei, Chongchong Guan
2018 IEEE Access  
INDEX TERMS Outlier detection, time series dataset, assembled outliers, cross-correlation analysis, multilevel Otsu's method.  ...  In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank.  ...  The outlier detection result and the cross-correlation function are shown in Fig.11 and Fig.12 , respectively.  ... 
doi:10.1109/access.2018.2870151 fatcat:ig2ttembcvcqxd4bqklytg5azi

An Outlier Detection Model Based on Cross Datasets Comparison for Financial Surveillance

Tianqing Zhu
2006 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06)  
Peer group analysis concept is largely dependent on a cross-datasets outlier detection model.  ...  In this paper, we propose a new cross outlier detection model based on distance dejinition incorporated with the financial transaction data features.  ...  In this paper we present a cross datasets outlier detection mode to fulfil the void.  ... 
doi:10.1109/apscc.2006.33 dblp:conf/apscc/Zhu06a fatcat:l556h45ijvfopiqqc2zeosc5ey

Robust methods for outlier detection and regression for SHM applications

Nikolaos Dervilis, Ifigeneia Antoniadou, Robert J. Barthorpe, Elizabeth J. Cross, Keith Worden
2015 International Journal of Sustainable Materials and Structural Systems  
Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions  ...  The discussion initially focuses on the high level removal of the 'masking effect' of inclusive outliers.  ...  , 2012; Cross et al., 2011) .  ... 
doi:10.1504/ijsmss.2015.078354 fatcat:hzal3zb4argmbnvxqmowfllaim

Cross-Sectional Analysis of Impulse Indicator Saturation Method for Outlier Detection Estimated via Regularization Techniques with Application of COVID-19 Data

Sara Muhammadullah, Amena Urooj, Muhammad Hashim Mengal, Shahzad Ali Khan, Fereshteh Khalaj, Diego Pinto
2022 Computational and Mathematical Methods in Medicine  
However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data.  ...  Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator.  ...  The concept of the IS method for outlier detection in the cross-sectional analysis would help to preserve unobserved heterogeneity in cross-sectional analysis, which simultaneously declines the RMSE of  ... 
doi:10.1155/2022/2588534 pmid:35529268 pmcid:PMC9073553 fatcat:y4w6zbr2jjhxdabv4upyarbz7a

Exploring the Power of Outliers for Cross-Domain Literature Mining [chapter]

Borut Sluban, Matjaž Juršič, Bojan Cestnik, Nada Lavrač
2012 Lecture Notes in Computer Science  
We have detected outlier documents by combining three classification-based outlier detection methods and explored the power of these outlier documents in terms of their potential for supporting the bridging  ...  In bisociative cross-domain literature mining the goal is to identify interesting terms or concepts which relate different domains.  ...  Classification Noise Filters for Outlier Detection The novelty of our work is to use noise detection approaches for findinging outlier documents containing cross-domain links (bridging terms -b-terms)  ... 
doi:10.1007/978-3-642-31830-6_23 fatcat:vunvpq7wbnfhpat56bu2aymssm

Cross Domain Image Matching in Presence of Outliers [article]

Xin Liu, Seyran Khademi, Jan C. van Gemert
2019 arXiv   pre-print
To this end, we propose an end-to-end architecture that can match cross domain images without labels in the target domain and handle non-overlapping domains by outlier detection.  ...  Extensive experimental evidence on Office [17] dataset and our proposed datasets Shape, Pitts-CycleGAN shows that the proposed approach yields state-of-the-art cross domain image matching and outlier detection  ...  Table 3 : 3 MAP performance for cross domain image matching with outlier detection on our three datasets. The proportion of outliers is 10%.  ... 
arXiv:1909.03552v1 fatcat:5qyivysxlrenhhxtjdnvnxzdha

Cross Domain Image Matching in Presence of Outliers

Xin Liu, Seyran Khademi, Jan Van Gemert
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
To this end, we propose an end-to-end architecture that can match cross domain images without labels in the target domain and handle non-overlapping domains by outlier detection.  ...  Extensive experimental evidence on Office [17] dataset and our proposed datasets Shape, Pitts-CycleGAN shows that the proposed approach yields state-of-the-art cross domain image matching and outlier detection  ...  Table 3 : 3 MAP performance for cross domain image matching with outlier detection on our three datasets. The proportion of outliers is 10%.  ... 
doi:10.1109/iccvw.2019.00406 dblp:conf/iccvw/LiuKG19 fatcat:u3el4yv4nfc7piyx4alhldht5i

Simple Detection Method and Compensation Filter to Remove Corner Outlier Artifacts [chapter]

Jongho Kim, Donghyung Kim, Jechang Jeong
2006 Lecture Notes in Computer Science  
We propose a simple detection method and compensation filter in order to remove corner outlier artifacts.  ...  The corner outlier artifacts are detected based on the directions of edges going through a block corner and the characteristics of blocks around the edges.  ...  Fig. 3 . 3 Arrangement of pixels and their indices around the cross-point for detecting and filtering corner outlier artifacts.  ... 
doi:10.1007/11867586_48 fatcat:kly3lvfz55dylo6sprvdf473bi

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data

Evie van der Spoel, Jungyeon Choi, Ferdinand Roelfsema, Saskia le Cessie, Diana van Heemst, Olaf M. Dekkers
2019 Journal of Biological Rhythms  
The performance of the methods was evaluated based on the number of outliers detected and the change in statistical outcomes after removing detected outliers.  ...  In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data.  ...  However, after removing outliers detected by the EM algorithm, cross-correlation decreased in most cases.  ... 
doi:10.1177/0748730419850917 pmid:31187683 pmcid:PMC6637814 fatcat:eplza3ywxfdwbizj4sky7xwufq

Multivariate Spatial Outlier Detection Using Robust Geographically Weighted Methods

Paul Harris, Chris Brunsdon, Martin Charlton, Steve Juggins, Annemarie Clarke
2013 Mathematical Geosciences  
Results clearly show value in both geographically weighted methods to outlier detection.  ...  Detection in principal components analysis space can also utilise goodness of fit distances. For spatial applications, however, these global forms can only detect outliers in a non-spatial manner.  ...  Simple CoK) as a basis to detect outliers. In and of itself, CoK is not a method to detect outliers, but CoK cross-validation can be used.  ... 
doi:10.1007/s11004-013-9491-0 fatcat:4vkvey4pb5eo7alxad4pc5bqdy
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