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*To*: AUDITORY@xxxxxxxxxxxxxxx*Subject*: Re: MDS distances*From*: Pierre Divenyi <pdivenyi@xxxxxxxxx>*Date*: Tue, 27 Jun 2006 11:41:23 -0700*Delivery-date*: Tue Jun 27 14:47:43 2006*In-reply-to*: <4ce21ddc0606271110w336acfa9pcd24aae97bc0956c@mail.gmail.co m>*References*: <200606151846.k5FIkE1G021309@manfred.music.uiuc.edu> <9c1a4ffb0606210211y40a45568wb428d93267e66b4@mail.gmail.com> <4499175D.6010303@imag.fr> <DD117415-D827-4597-BCE4-9CA2CECB0BC7@ieee.org> <4ce21ddc0606271110w336acfa9pcd24aae97bc0956c@mail.gmail.com>*Reply-to*: Pierre Divenyi <pdivenyi@xxxxxxxxx>*Sender*: AUDITORY Research in Auditory Perception <AUDITORY@xxxxxxxxxxxxxxx>

Jean-Francois,

Pierre

One should not try to interpret the "meaning" of MDS dimensions, since any rotation of an MDS solution is a completely equivalent solution. Hence, looking at the vectors components of an MDS solution has no sense unless you find a way to fix some dimensions in a meaningful way. That's why different MDS algorithms can lead to different (valid) solutions given the same initial similarity matrix. If your goal is to find the "intrinsic" dimensions of sound data, my opinion is that it would be preferable to use state-of-the-art dimensionality reduction algorithms (Isomap, LLE, non-local techniques, or even PCA), on a set of points obtained from MDS with no loss in higher dimension.

-- Jean-François Paiement Research Assistant IDIAP Research Institute Martigny, Switzerland paiement@xxxxxxxx

**Follow-Ups**:**Re: MDS distances***From:*Bruno L. Giordano

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

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

**Re: MDS distances***From:*Olivier Tache

**Re: MDS distances***From:*Malcolm Slaney

**Re: MDS distances***From:*Jean-François Paiement

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