Perhaps "un-morphometrics". In morphometrics we discard location, orientation,
and size and analyze what variation is left - shape. Here it sounds like you
want the opposite.
Jim
-------
Sent remotely by F. James Rohlf,
John S. Toll Professor, Stony Brook University
-----Original Message-----
From: "Shannon, William" <wshan...@dom.wustl.edu>
Sender: "Classification, clustering, and phylogeny estimation"
<CLASS-L@lists.sunysb.edu>
Date: Tue, 15 May 2012 18:55:15
To: <CLASS-L@lists.sunysb.edu>
Reply-To: "Classification, clustering, and phylogeny estimation"
<CLASS-L@lists.sunysb.edu>
Subject: Re: clustering in a time series
Jim
I was thinking similarly that the goal is to keep the head of the person being
imaged (using MEG) completely immobile. If they are running multiple scans
then the registration of one image with the next is greatly simplified by
knowing the brain has not moved.
I also was thinking that morphometric techniques might be useful. Instead of
transforming one set of morphometric measurements onto another set, they could
use these to test that no transformation is required.
Marc, does this sound right?
Thank you
Bill Shannon, PhD, MBA (In Progress)
Professor of Biostatistics in Medicine
Washington University School of Medicine
Director, Biostatistical Consulting Center
314-454-8356
________________________________
From: Classification, clustering, and phylogeny estimation
[CLASS-L@LISTS.SUNYSB.EDU] On Behalf Of F. James Rohlf
[ro...@life.bio.sunysb.edu]
Sent: Tuesday, May 15, 2012 6:39 PM
To: CLASS-L@LISTS.SUNYSB.EDU
Subject: Re: clustering in a time series
I am not exactly what question is being asked. I assume the head is rigid. So
the 3 distances should be invariant except for measurement error. To detect
movement one would want to detect changes in the location of the 3 points in
space and their orientation would also be of interest. Does that sound
reasonable?
-------
Sent remotely by F. James Rohlf,
John S. Toll Professor, Stony Brook University
________________________________
From: Marc Lalancette <marc.lalance...@sickkids.ca>
Sender: "Classification, clustering, and phylogeny estimation"
<CLASS-L@lists.sunysb.edu>
Date: Tue, 15 May 2012 21:05:15 +0000
To: <CLASS-L@lists.sunysb.edu>
ReplyTo: "Classification, clustering, and phylogeny estimation"
<CLASS-L@lists.sunysb.edu>
Subject: clustering in a time series
Hello,
I was referred to this list by someone on sci.stat.math
(https://groups.google.com/forum/?fromgroups#!topic/sci.stat.math/-bDEys5WTjk).
I apologize if this is not the right forum for this type of question. I have
limited stats knowledge and I've been doing some research to find a "good"
solution to my problem. I'll first describe what I want to do and then what I
came up with based on some more or less fruitful research. I'd appreciate some
suggestions, tips, references to similar or "better" methods, etc.
I have 3-d motion data for 3 markers stuck on a person's head, while he/she is
trying to be still. The first step is making sure the markers did not fall
off, so I calculate the 3 between marker distances across time and in what
follows I basically treat that as a 3-d Euclidean vector, even though it's not
Euclidean, but I'm not sure how else to combine these 3 distances... (Also,
later, I could ask the same questions about each marker's position to see if
the person moved and in that case it is Euclidean 3-d space.) I want to detect
changes, and get a "good" partition of the time series into intervals based on
when changes occurred, i.e. a list of roughly stationary intervals and an
associated position for each interval. I'm assuming there won't be many
changes and that they would mostly be fast (i.e. step-like time series), but
they could also be slow, in which case I'd still want to detect it and split it
in chunks that are "mostly still", depending on the measurement error.
After some research, here's what I came up with. My first idea was to use 3-d
distances between samples adjacent in time to evaluate the measurement error,
i.e. an approximation of the distribution if there were no movement (that's why
the assumption of few changes is important). Comparing that distribution with
the distribution of all inter-point distances (or a subset: maybe all distances
from the first point) would tell me if there was any movement. I was thinking
of using the Kolmogorov-Smirnov test for this. Then I thought I could use a
hierarchical clustering method based on the same idea. Divisive since I expect
few clusters. I would recursively look for the boundary point in time that
would maximize the KS test probability for the intervals on both sides
(one-sided test since intervals that are unusually stationary would be "ok").
Then I looked for something that would account for model complexity (thinking
of reduced chi-square) and found the AIC. Maybe I could interpret the combined
KS probabilities as a likelihood for that particular partition and use the AIC
to decide when to stop dividing the intervals.
This is what I came up with based on what I found in my research. Almost all
of the concepts and methods I mention I didn't know about a week ago, so I
assume the resulting amalgamation has quite a few "weaknesses" even though it
might work. I'd be happy to hear what knowledgeable people would have to say
about this. Feel free to contact me directly by email. I'll also monitor the
list for replies.
Thanks!
Marc Lalancette
Research MEG Lab Project Manager
Program in Neurosciences and Mental Health, Department of Diagnostic Imaging,
The Hospital for Sick Children, Toronto, Canada
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