-------- Original Message --------
Subject: Re: PCA with VERY large number of landmarks?
Date: Tue, 4 Oct 2011 15:46:00 -0400
From: F. James Rohlf <[email protected]>
Reply-To: [email protected]
To: Morphmet <[email protected]>
No curse of dimensionality until you perform a multivariate test. The
PCA can be viewed as just dimension reduction. More math than stats.
-------
Sent remotely by F. James Rohlf,
John S. Toll Professor
------------------------------------------------------------------------
*From: * morphmet <[email protected]>
*Date: *Tue, 04 Oct 2011 15:27:13 -0400
*To: *morphmet<[email protected]>
*ReplyTo: * [email protected]
*Subject: *PCA with VERY large number of landmarks?
-------- Original Message --------
Subject: PCA with VERY large number of landmarks?
Date: Mon, 3 Oct 2011 21:48:03 -0400
From: Adam Douglas Yock <[email protected]>
To: [email protected]
Hello,
I am new to the field of morphometrics and have a (potentially very
ignorant) question.
I have images that contain a deformable body and a rigid body. The
images are rigidly registered to align the rigid bodies. The deformable
bodies are described by ~5,000 points which are matched across each
image. I believe my data is then comprised of the 3D coordinates of the
~5,000 points of the deformable body depicted in each image.
Can I treat these points as landmarks and perform a very
high-dimensional (~15,000-D) PCA? Is there any "curse of dimensionality"
with this method?
I appreciate your help.
Adam
[email protected] <mailto:[email protected]>
