-------- Original Message --------
Subject: Re: PCA with VERY large number of landmarks?
Date: Wed, 5 Oct 2011 11:18:40 -0400
From: Dennis E. Slice <[email protected]>
To: [email protected]
For a similar application involving pk=25,137 dimensions and PCOORD, see:
Ross, Callum F, Biren A Patel, Dennis E Slice, David S Strait, Paul C
Dechow, Brian G Richmond, and Mark A Spencer. 2005. "Modeling
masticatory muscle force in finite element analysis: sensitivity
analysis using principal coordinates analysis." /The Anatomical Record.
Part A, Discoveries in Molecular, Cellular, and Evolutionary Biology/
283 (2) (April): 288-299. doi:10.1002/ar.a.20170.
PCOORD in this case turned it from a 25k^2 problem into an analysis of a
37x37 matrix. With PCOORD, you loose the loadings for the individual
variables you get from PCA, but you get the same low-dimensional plots,
scores, etc. (up to reflections). The PCOORD axes can be visualized by
regression of the original (GPA'd) data onto the individual PCOORD axis
scores. I do such things for 3D (actually, k-dimensional) data with
various versions of Morpheus and R.
-ds
On 10/4/11 3:27 PM, morphmet wrote:
-------- 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]>
--
Dennis E. Slice
Associate Professor
Dept. of Scientific Computing
Florida State University
Dirac Science Library
Tallahassee, FL 32306-4120
-
Guest Professor
Department of Anthropology
University of Vienna
-
Software worth having/learning/using...
Linux (Operating System: Ubuntu, CentOS, openSUSE, etc.)
OpenOffice (Office Suite:http://www.openoffice.org/)
R package (Stats/Graphics environment:http://www.r-project.org/)
Eclipse (Java/C++/etc IDE:http://www.eclipse.org/)
Netbeans (Java/C++/etc IDE:http://netbeans.org/)
Zotero (FireFox bibliographic extension:http://www.zotero.org/)
========================================================
