Expected similarity estimation for large-scale batch and streaming anomaly detection
Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schneider, Markus [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Springer US, 1986, 105(2016), 3 vom: 18. Mai, Seite 305-333 |
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| Übergeordnetes Werk: |
volume:105 ; year:2016 ; number:3 ; day:18 ; month:05 ; pages:305-333 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10994-016-5567-7 |
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OLC202652694X |
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| 520 | |a Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. | ||
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| 700 | 1 | |a Ramos, Fabio |4 aut | |
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10.1007/s10994-016-5567-7 doi (DE-627)OLC202652694X (DE-He213)s10994-016-5567-7-p DE-627 ger DE-627 rakwb eng 150 004 VZ Schneider, Markus verfasserin aut Expected similarity estimation for large-scale batch and streaming anomaly detection 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. Anomaly detection Large-scale data Kernel methods Hilbert space embedding Mean map Ertel, Wolfgang aut Ramos, Fabio aut Enthalten in Machine learning Springer US, 1986 105(2016), 3 vom: 18. Mai, Seite 305-333 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:105 year:2016 number:3 day:18 month:05 pages:305-333 https://doi.org/10.1007/s10994-016-5567-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4318 AR 105 2016 3 18 05 305-333 |
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10.1007/s10994-016-5567-7 doi (DE-627)OLC202652694X (DE-He213)s10994-016-5567-7-p DE-627 ger DE-627 rakwb eng 150 004 VZ Schneider, Markus verfasserin aut Expected similarity estimation for large-scale batch and streaming anomaly detection 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. Anomaly detection Large-scale data Kernel methods Hilbert space embedding Mean map Ertel, Wolfgang aut Ramos, Fabio aut Enthalten in Machine learning Springer US, 1986 105(2016), 3 vom: 18. Mai, Seite 305-333 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:105 year:2016 number:3 day:18 month:05 pages:305-333 https://doi.org/10.1007/s10994-016-5567-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4318 AR 105 2016 3 18 05 305-333 |
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10.1007/s10994-016-5567-7 doi (DE-627)OLC202652694X (DE-He213)s10994-016-5567-7-p DE-627 ger DE-627 rakwb eng 150 004 VZ Schneider, Markus verfasserin aut Expected similarity estimation for large-scale batch and streaming anomaly detection 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. Anomaly detection Large-scale data Kernel methods Hilbert space embedding Mean map Ertel, Wolfgang aut Ramos, Fabio aut Enthalten in Machine learning Springer US, 1986 105(2016), 3 vom: 18. Mai, Seite 305-333 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:105 year:2016 number:3 day:18 month:05 pages:305-333 https://doi.org/10.1007/s10994-016-5567-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4318 AR 105 2016 3 18 05 305-333 |
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Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. © The Author(s) 2016 |
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Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. © The Author(s) 2016 |
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Abstract We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches. © The Author(s) 2016 |
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| up_date |
2024-07-04T04:10:09.534Z |
| _version_ |
1803620145728323584 |
| fullrecord_marcxml |
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The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with exposecan be done in linear time and online (incremental) learning takes constant time per instance and model update. Furthermore, exposecan make predictions in constant time, while it requires only constant memory. In addition, we propose different methodologies for concept drift adaptation on evolving data streams. On several real datasets we demonstrate that our approach can compete with state of the art algorithms for anomaly detection while being an order of magnitude faster than most other approaches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Anomaly detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Large-scale data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kernel methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hilbert space embedding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mean map</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ertel, Wolfgang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramos, Fabio</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Machine learning</subfield><subfield code="d">Springer US, 1986</subfield><subfield code="g">105(2016), 3 vom: 18. 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7.401326 |