[ncl-talk] Canonical Correlation Analysis via Barnett and Preisendorfer (BPCCA)

[Dennis Shea]

*I have no comment on the Barnett & Priesendorfer canonical correlation.*
*Somebody else will have to comment.*

===
re: "... the built-in “cancor” (which fails often) ..."

This is true. There is an issue with NCL's 'cancor function.
In fact, there is a 'trouble ticket' on this from **2010**:
      JIRA 589: Possible bug in CANCOR

My recollection is that It works correctly for "smaller" array sizes but
fails when some critical size is reached.
Obviously,  investigating this issue has been put on the back-burner.
-----

FYI: NCL's canonical correlation function uses a 'double precision' version
of the f77 'SUBROUTINE CANOR' from the IBM Scientific Software Package
[SPP].scientific
library.

  http://pdp-10.trailing-edge.com/decuslib20-02/01/decus/20-
0026/canor.ssp.html

----
Actually, NCL has two hidden 'interfaces' between the NCL code and the f77
subroutine. :

  (a) a f77subroutine (written by me)
  (b) a C-language interface between the NCL code and the fortran interface
code (a)
----
**Maybe**, I will take a look

Cheers







On Sun, Nov 12, 2017 at 12:53 PM, Xiaoning Wu <xiaoning.wu.1 at stonybrook.edu>
wrote:

> Hello,
>
> On a statistical application of NCL -  has anyone tried coding up a script
> to perform the Barnett and Preisendorfer approach of Canonical Correlation
> Analysis (BPCCA) between two datasets?
>
> Compared to the built-in “cancor” (which fails often), the trick with
> BPCCA is to  decompose the data with PCA/EOF first, and the subsequent
> linear algebra would involve the dominant modes only, thus bypassing direct
> inversion of the original (large) matrices which is presumably where the
> classical approach usually gets stuck. The method looks pretty manageable,
> with existing matlab scripts to refer to, but I'm trying to avoid
> reinventing the wheel here assuming it must have been done before in NCL.
> Any thoughts would be very much appreciated!
>
> Thank you for your time. Have a great week!
>
> Best regards,
> Xiaoning
>
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