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## Re: memory

**From**: |
Martin Helm |

**Subject**: |
Re: memory |

**Date**: |
Wed, 06 Apr 2011 16:22:15 +0200 |

Am Mittwoch, den 06.04.2011, 10:11 +0530 schrieb nuncio m:
HI martin,
>* Thanks, but I need to find the covariance matrix *
>* separately. Because as a next step I need to find the svd of coupled*
>* fields. for example atmospheric pressure and temperature. For a *
>* single matrix I can find eigen vectors using SVD but I understand *
>* this *
>* is not possible without computing the covariance matrix for two *
>* separate matrices. *
>* nuncio*
>* *
Dear Nuncio,
sorry that I insist so much. I do of course not know your problem at
hand, but have my doubts about the matrix you compute (the product which
blows up to about 30000x30000). This matrix contains essentially no
information, more than 29000 of its eigen values are exact zero, more
than 29000 of its eigen vectors are simply a representation of the null
space since it has a maximal rank of 348.
Do you really have nearly 30000 variables and only 348 measurements?
- Martin

**memory**, *nuncio m*, `2011/04/05`
**Re: memory**, *Martin Helm*, `2011/04/05`
**Re: memory**, *nuncio m*, `2011/04/05`
**Re: memory**, *Moo*, `2011/04/05`
**Re: memory**, *nuncio m*, `2011/04/05`
**Re: memory**, *Martin Helm*, `2011/04/05`
*Message not available***Re: memory**,
*Martin Helm* **<=**
*Message not available***Re: memory**, *Martin Helm*, `2011/04/08`
**Re: memory**, *nuncio m*, `2011/04/09`
**Re: memory**, *Martin Helm*, `2011/04/09`
**Re: memory**, *nuncio m*, `2011/04/10`
**Re: memory**, *Martin Helm*, `2011/04/05`