False nearest neighbor algorithm - Revision history

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← Previous revision		Revision as of 00:33, 30 March 2023
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	Within [[abstract algebra]], the '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al. (1992).<ref>{{cite journal \|last1=Kennel \|first1=Matthew B. \|last2=Brown \|first2=Reggie \|last3=Abarbanel \|first3=Henry D. I. \|title=Determining embedding dimension for phase-space reconstruction using a geometrical construction \|journal=Physical Review A \|date=1 March 1992 \|volume=45 \|issue=6 \|pages=3403–3411 \|doi=10.1103/PhysRevA.45.3403}}</ref> The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.<ref>{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| author-link2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}</ref><ref>{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}</ref>		Within [[abstract algebra]], the '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al. (1992).<ref>{{cite journal \|last1=Kennel \|first1=Matthew B. \|last2=Brown \|first2=Reggie \|last3=Abarbanel \|first3=Henry D. I. \|title=Determining embedding dimension for phase-space reconstruction using a geometrical construction \|journal=Physical Review A \|date=1 March 1992 \|volume=45 \|issue=6 \|pages=3403–3411 \|doi=10.1103/PhysRevA.45.3403\|pmid=9907388 \|bibcode=1992PhRvA..45.3403K }}</ref> The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.<ref>{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| author-link2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}</ref><ref>{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}</ref>

	==See also==		==See also==

Neuropsychologist: Put references at their place

2022-04-07T01:33:28Z

Put references at their place

← Previous revision		Revision as of 01:33, 7 April 2022
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	Within [[abstract algebra]], the '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al.. The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.		Within [[abstract algebra]], the '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al. (1992).<ref>{{cite journal \|last1=Kennel \|first1=Matthew B. \|last2=Brown \|first2=Reggie \|last3=Abarbanel \|first3=Henry D. I. \|title=Determining embedding dimension for phase-space reconstruction using a geometrical construction \|journal=Physical Review A \|date=1 March 1992 \|volume=45 \|issue=6 \|pages=3403–3411 \|doi=10.1103/PhysRevA.45.3403}}</ref> The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.<ref>{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| author-link2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}</ref><ref>{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}</ref>

	==See also==		==See also==
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	==References==		==References==
			{{Reflist}}
	*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| author-link2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}

	*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}
	*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\|bibcode = 1992PhRvA..45.3403K }}

	[[Category:Statistical algorithms]]		[[Category:Statistical algorithms]]

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		⚫	Within [[abstract algebra]], the '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al.. The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.
	{{context\|date=October 2009}}
⚫	~~The~~ '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al.. The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.

	==See also==		==See also==

JoeNMLC: /* See also */ add Local ring

2022-02-15T17:39:11Z

JoeNMLC: /* See also */ add Commutative ring, alpha. seq.

2022-02-15T17:38:00Z

See also: add Commutative ring, alpha. seq.

← Previous revision		Revision as of 17:38, 15 February 2022
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	==See also==		==See also==
	* [[~~Time~~ ~~series~~]]		* [[Commutative ring]]
	* [[Nearest neighbor (disambiguation)\|Nearest neighbor]]		* [[Nearest neighbor (disambiguation)\|Nearest neighbor]]
			* [[Time series]]

	==References==		==References==

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2020-12-06T21:41:15Z

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	==References==		==References==
	*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| ~~authorlink2~~=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}		*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| author-link2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}
	*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}		*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}
	*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388~~\| pmc =~~ \|bibcode = 1992PhRvA..45.3403K }}		*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\|bibcode = 1992PhRvA..45.3403K }}

	[[Category:Statistical algorithms]]		[[Category:Statistical algorithms]]

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← Previous revision		Revision as of 22:15, 16 May 2020
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	==References==		==References==
	*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| authorlink2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}		*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| authorlink2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}
	*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = ~~4970~~ \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}		*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970–3 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}
	*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}		*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}

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	==References==		==References==
	*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| authorlink2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = ~~S1149~~ \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}		*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| authorlink2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149–S1154 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}
	*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\|bibcode = 1999PhRvE..60.4970H }}		*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\| pmid = 11970367 \|bibcode = 1999PhRvE..60.4970H }}
	*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}		*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}

Kku: authorlink

2019-09-19T09:44:14Z

authorlink

← Previous revision		Revision as of 09:44, 19 September 2019
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	{{context\|date=October 2009}}		{{context\|date=October 2009}}
	The '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al. The main idea is to examine how the number of neighbors of a point along a signal trajectory change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.		The '''false nearest neighbor algorithm''' is an [[algorithm]] for estimating the [[embedding dimension]]. The concept was proposed by Kennel et al.. The main idea is to examine how the number of neighbors of a point along a signal [[trajectory]] change with increasing embedding dimension. In too low an embedding dimension, many of the neighbors will be false, but in an appropriate embedding dimension or higher, the neighbors are real. With increasing dimension, the false neighbors will no longer be neighbors. Therefore, by examining how the number of neighbors change as a function of dimension, an appropriate embedding can be determined.

	==See also==		==See also==
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	==References==		==References==
	*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}		*{{Cite journal \| last1 = Rhodes \| first1 = C. \| last2 = Morari \| first2 = M. \| authorlink2=Manfred Morari\|title = The false nearest neighbors algorithm: An overview \| journal = Computers & Chemical Engineering \| volume = 21 \| pages = S1149 \| year = 1997 \| doi = 10.1016/S0098-1354(97)87657-0}}
	*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\|bibcode = 1999PhRvE..60.4970H }}		*{{Cite journal \| last1 = Hegger \| first1 = R. \| last2 = Kantz \| first2 = H. \| title = Improved false nearest neighbor method to detect determinism in time series data \| journal = Physical Review E \| volume = 60 \| issue = 4 \| pages = 4970 \| year = 1999 \| doi = 10.1103/PhysRevE.60.4970\|bibcode = 1999PhRvE..60.4970H }}
	*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}		*{{Cite journal \| last1 = Kennel \| first1 = M. \| last2 = Brown \| first2 = R. \| last3 = Abarbanel \| first3 = H. \| doi = 10.1103/PhysRevA.45.3403 \| title = Determining embedding dimension for phase-space reconstruction using a geometrical construction \| journal = Physical Review A \| volume = 45 \| issue = 6 \| pages = 3403–3411 \| year = 1992 \| pmid = 9907388\| pmc = \|bibcode = 1992PhRvA..45.3403K }}

Natg 19: Disambiguated: Nearest neighbor → Nearest neighbor (disambiguation)

2017-12-14T10:26:15Z

Disambiguated: Nearest neighbor → Nearest neighbor (disambiguation)

← Previous revision		Revision as of 10:26, 14 December 2017
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	==See also==		==See also==
	* [[Time series]]		* [[Time series]]
	* [[Nearest neighbor~~]]{{dn~~\|~~date=December~~ ~~2017}}~~		* [[Nearest neighbor (disambiguation)\|Nearest neighbor]]

	==References==		==References==

← Previous revision		Revision as of 17:39, 15 February 2022
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	==See also==		==See also==
	* [[Commutative ring]]		* [[Commutative ring]]
			* [[Local ring]]
	* [[Nearest neighbor (disambiguation)\|Nearest neighbor]]		* [[Nearest neighbor (disambiguation)\|Nearest neighbor]]
	* [[Time series]]		* [[Time series]]