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Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
[article]
2021
arXiv
pre-print
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In this work, we target the textual anomaly detection problem and propose a deep anomaly-injected support vector data description (AI-SVDD) framework. ...
Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years. ...
Acknowledgments We would like to show our special thanks to Xingyuan Pan, who is a Ph.D. graduated from University of Utah and now working as an applied scientist in Amazon. ...
arXiv:2110.14729v1
fatcat:iw42w6wr4vhark663chove646m
CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems
[article]
2019
arXiv
pre-print
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are visualized in our system to indicate the occurrences of anomalies. ...
A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern), the results of which ...
, from data centers to their sub-level data clusters; the temporal overview ( Fig. 4(2) ), displaying the anomaly distributions over time for data filtering (T1); the rank view ( Fig. 4(3) ), showing ...
arXiv:1907.13187v1
fatcat:fai2vqpxrfdb7gahlovxiwivsi
Hyperspectral Anomaly Detection via Dual Collaborative Representation
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Index Terms-Collaborative representation, density peak (DP) clustering, hyperspectral anomaly detection, low-rank and sparse matrix decomposition (LRSMD). ...
Then, the density peak clustering algorithm is applied to the low-rank background matrix to calculate the density information of the pixels in a sliding dual window. ...
In matrix completion, a low-rank matrix can be quickly recovered from a small amount of randomly sampled data. ...
doi:10.1109/jstars.2020.3009324
fatcat:stvf7x2tw5bnvm532jwoey76wu
Data stream anomaly detection through principal subspace tracking
2010
Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10
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We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking). ...
It automatically learns the principal subspace from N numerical data streams and an anomaly is indicated by a change in the number of latent variables. ...
In data centers, an anomaly is a short-lived deviation from its normal operation. ...
doi:10.1145/1774088.1774434
dblp:conf/sac/TeixeiraM10
fatcat:m6oui6ezojhvxihyoorkd7gq6a
Tracking System Behaviour from Resource Usage Data
[article]
2017
arXiv
pre-print
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Results are shown for data collected for 2013 from the Lonestar4 system at the Texas Advanced Computing Center (TACC) ...
in the system logs. ...
Additionally, the authors thank researchers at Texas Advanced Computing Center for access to the Lonestar4 data and related support. ...
arXiv:1705.10756v1
fatcat:q724ertflbdkfbhvy23jmsuvdi
Seasonal influence of ENSO on the Atlantic ITCZ and equatorial South America
2005
Geophysical Research Letters
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Data Sets and Methods [6] We analyze satellite based monthly data for precipitation (Climate Prediction Center Merged Analysis of Precipitation ''CMAP'' [Xie and Arkin, 1997] ), SST (National Oceanic ...
and Atmospheric Administration ''OI.v2'' [Reynolds et al., 2002] ) and sea level height (AVISO/altimetry data center ''DT-MSLA'' [Ducet et al., 2000] ) which start in 1979, 1982 and 1993, respectively ...
doi:10.1029/2005gl023900
fatcat:ppuguoeh45bevnja6n47s3cwya
Comparison of 1997–98 U.S. Temperature and Precipitation Anomalies to Historical ENSO Warm Phases
1999
Journal of Climate
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For 1997 and 1998, daily precipitation totals and temperature means are obtained from the sum- mary of the day (SOD) data available through the Na- tional Climatic Data Center. ...
Ranking the anomalies versus nine historical warm phases identifies extreme 1998 sea- sonal anomalies and places these anomalies in a his- torical context. ...
doi:10.1175/1520-0442(1999)012<3507:cousta>2.0.co;2
fatcat:qnpvs57xq5bvbpjhyodg4afejm
Page 1852 of Journal of Climate Vol. 12, Issue 6
[page]
1999
Journal of Climate
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The temporal separation, Af, is ex- pressed in kilometers using
_ time difference in days X spatial radius in km
At ea temporal radius in days
(4)
The resulting cluster centers were then ranked first in ...
An SST anomaly for each observation in each of the two in situ datasets is then obtained for each of the six climatologies, resulting in 12 anomaly datasets. An anomaly, SST!. ...
K-Means-based isolation forest
2020
Knowledge-Based Systems
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There may be incorrect data present in the database, e.g., mistakenly inserted by users. Also the distant points located far from cluster centers could be regarded as anomalies. ...
these rankings of points in a context of anomaly score, see Fig. 12 and Fig. 13 . ...
doi:10.1016/j.knosys.2020.105659
fatcat:m4hd345oengw5nbpo7c4frj7ly
Evaluation of IPCC Models' Performance in Simulating Late-Twentieth-Century Climatologies and Weather Patterns over North America
2011
Journal of Climate
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Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern. ...
However, the model ranking is sensitive to the choice of climate variable. ...
Flato generously provided the NARR data. Furthermore, we thank A. Rasmussen, A. Werner, and the three anonymous reviewers for their valuable comments. ...
doi:10.1175/jcli-d-11-00011.1
fatcat:tiv3yzouqfhvzcv23cvrguepre
Flow-based Anomaly Detection
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2010.03002v1 fatcat:4vfrbtq3ire3bhzx5yefxos6xyOther Versions
Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems. ...
We propose OneFlow - a flow-based one-class classifier for anomaly (outliers) detection that finds a minimal volume bounding region. ...
In a similar spirit, Chen, Qian, and Saligrama (2013) apply Ranking SVM based on rankings created from pairwise comparison of nominal data. ...
arXiv:2010.03002v2
fatcat:4wjue5yuunewbpalw5yxx56oyy
Event detection using customer care calls
2013
2013 Proceedings IEEE INFOCOM
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., thousands of categories in our dataset). In this paper, we propose a systematic method for detecting events in a major cellular network using customer care call data. ...
We show the effectiveness of our approach using data from a large cellular service provider in the US. ...
Acknowledgements: This work is supported in part by NSF Grants CNS-0916309 and CNS-1117009. ...
doi:10.1109/infcom.2013.6566966
dblp:conf/infocom/ChenLDQW13
fatcat:vpzhxdujnjhhtajxkroqybogny
A data-driven approach to detect passenger flow anomaly under station closure
2020
IEEE Access
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The Wilcoxon signed rank test requires data pairing. ...
Since the travel time data may be subject to different distributions, the Wilcoxon signed rank test has a wider scope of application than the T-test in travel time anomaly testing applicability. ...
doi:10.1109/access.2020.3016398
fatcat:tttigiuehbbkfpetbq7uhy5snq
Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool
[article]
2020
arXiv
pre-print
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This work introduces Chimbuko, a performance analysis framework that provides real-time, distributed, in situ anomaly detection. ...
Data volumes are reduced for human-level processing without losing necessary details. ...
Then, the scientist switched to Rank 0 and wanted to check how "MD FORCES" may affect other ranks. He found that Rank 0 mainly suffered anomalies in "MD FINIT" (Fig. 11 and "CF CMS" (Fig. 12 ). ...
arXiv:2008.13742v1
fatcat:wc2ltfxpvbddvfycsgquysapwi
Flow-based SVDD for anomaly detection
[article]
2021
arXiv
pre-print
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We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. ...
In the latent space, FlowSVDD finds the center point c and radius R to enclose (1 − ν) percentage of data inside the ball B(c; R). ...
Observe that, unlike the density-based flow models, FlowSVDD does not transform data into Gaussian distribution in a latent space. Benchmark data for anomaly detection. ...
arXiv:2108.04907v1
fatcat:lyye2mtn7jeafjisft4medb4rq
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