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20231224012722.0 |
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231224s1987 xx ||||| 00| ||eng c |
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|a pubmed24n0703.xml
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|a (DE-627)NLM21101642X
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|a (NLM)21869384
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Haimi-Cohen, R
|e verfasserin
|4 aut
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| 245 |
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|a Gradient-type algorithms for partial singular value decomposition
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| 264 |
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1 |
|c 1987
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 02.10.2012
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| 500 |
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|a Date Revised 12.11.2019
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| 500 |
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|a published: Print
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| 500 |
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a It is often desirable to calculate only a few terms of the SVD expansion of a matrix, corresponding to the largest or smallest singular values. Two algorithms, based on gradient and conjugate gradient search, are proposed for this purpose. SVD is computed term by term in a decreasing or increasing order of singular values. The algorithms are simple to implement and are especially advantageous with large matrices
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Cohen, A
|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 9(1987), 1 vom: 01. Jan., Seite 137-42
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:9
|g year:1987
|g number:1
|g day:01
|g month:01
|g pages:137-42
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| 912 |
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| 912 |
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|a GBV_ILN_350
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| 951 |
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|a AR
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| 952 |
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|d 9
|j 1987
|e 1
|b 01
|c 01
|h 137-42
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