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20250213044532.0 |
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231224s1979 xx ||||| 00| ||eng c |
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|a (NLM)21868828
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Pettis, K W
|e verfasserin
|4 aut
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|a An intrinsic dimensionality estimator from near-neighbor information
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|c 1979
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 02.10.2012
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|a Date Revised 12.11.2019
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a The intrinsic dimensionality of a set of patterns is important in determining an appropriate number of features for representing the data and whether a reasonable two- or three-dimensional representation of the data exists. We propose an intuitively appealing, noniterative estimator for intrinsic dimensionality which is based on nearneighbor information. We give plausible arguments supporting the consistency of this estimator. The method works well in identifying the true dimensionality for a variety of artificial data sets and is fairly insensitive to the number of samples and to the algorithmic parameters. Comparisons between this new method and the global eigenvalue approach demonstrate the utility of our estimator
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|a Journal Article
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|a Bailey, T A
|e verfasserin
|4 aut
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|a Jain, A K
|e verfasserin
|4 aut
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| 700 |
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|a Dubes, R C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 1(1979), 1 vom: 01. Jan., Seite 25-37
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:1
|g year:1979
|g number:1
|g day:01
|g month:01
|g pages:25-37
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|a AR
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|d 1
|j 1979
|e 1
|b 01
|c 01
|h 25-37
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