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*To*: AUDITORY@xxxxxxxxxxxxxxx*Subject*: Re: MDS distances*From*: Sylvain Choisel <sc@xxxxxxxxxxxxxxxx>*Date*: Wed, 21 Jun 2006 11:47:46 +0200*Delivery-date*: Wed Jun 21 05:52:48 2006*In-reply-to*: <9c1a4ffb0606210211y40a45568wb428d93267e66b4@mail.gmail.com>*References*: <200606151846.k5FIkE1G021309@manfred.music.uiuc.edu> <9c1a4ffb0606210211y40a45568wb428d93267e66b4@mail.gmail.com>*Reply-to*: Sylvain Choisel <sc@xxxxxxxxxxxxxxxx>*Sender*: AUDITORY Research in Auditory Perception <AUDITORY@xxxxxxxxxxxxxxx>*User-agent*: Thunderbird 1.5.0.4 (Windows/20060516)

Have you tried with ALSCAL? http://forrest.psych.unc.edu/research/alscal.html

If you want to avoid the trouble of formatting your data for ALSCAL, you can call it from Matlab with the following wrapper:

http://acoustics.aau.dk/~sc/matlab/alscal.html

Best regards,

Sylvain

Dear Jim,

I think the motivation behind your MDS question is a very interesting one. To me it seems that there are two likely possibilities.

-one is that "auditory dissimilarity" is truly quite high-dimensional, so Matlab did not manage to project the data into a low dimensional space because it simply cannot be done, and even reducing the dimensionality just a little bit from 10 to 8 leads to appreciable distoritions (i.e. "error"),

-the other (which seems to be the possibility you are asking about) is that Matlab's algorithm doesn't work properly. However, you could quite easily "sanity check" Matlab's algorithm: Just draw ten points randomly on (2-dimensional) graph paper, measure their pairwise distances, and feed those into the Matlab MDS algorithm. If Matlab does its job right it should give you a 2 D solution with errors no larger than the measurement errors you would expect from holding your ruler up to the points on your paper. In fact, it should be easily be possible to "virtualize" and automate his sort of sanity check by letting random numbers generate points in space of any dimensionality you choose and work out relative pairwise distances using Pythagoras. Once you have automated code that does this you could run hundreds of sanity checks like that you should know pretty quickly how far you can trust Matlab's algorithm. And I hope you will let us know the answer. I, for one, would not be too surprised if it turned out that sounds could sound dissimilar on at least 8 "different dimensions".

Best wishes,

Jan

On 15/06/06, *beaucham* <beaucham@xxxxxxxxxxxxxxxxxxxxxx <mailto:beaucham@xxxxxxxxxxxxxxxxxxxxxx>> wrote:

We ran an MDS calculation (using MatLab) on a 10x10 distance matrix based on dissimilarity judgements between all pairs of 10 sounds, and obtained an 8-dimension solution, which gives the coordinates of the sounds in 8-D space. The distances between the positions of the sounds are supposed to match the original distances. In fact, we get an rms error of 8% and a max error of 30%.

Is this typical? Is MatLab's program accurate? Is there a way to improve on the MDS results?

Jim Beauchamp Univ. of Illinois Urbana-Champaign jwbeauch@xxxxxxxx <mailto:jwbeauch@xxxxxxxx>

-- Dr Jan Schnupp University of Oxford Dept. of Physiology, Anatomy and Genetics Sherrington Building - Parks Road Oxford OX1 3PT - UK +44-1865-272513 www.oxfordhearing.com <http://www.oxfordhearing.com>

**References**:**MDS distances***From:*beaucham

**Re: MDS distances***From:*Jan Schnupp

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