Rolf Turner r.turner at auckland.ac.nz writes:
On 10/31/13 23:14, Takatsugu Kobayashi wrote:
I am struggling to come up with an efficient vectorized way to convert
20Kx20K covariance matrix to a Euclidian distance matrix as a surrogate for
dissimilarity matrix. Hopefully I can apply
Hi RUsers,
I am struggling to come up with an efficient vectorized way to convert
20Kx20K covariance matrix to a Euclidian distance matrix as a surrogate for
dissimilarity matrix. Hopefully I can apply multidimensional scaling for
mapping these 20K points (commercial products).
I understand that
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of Takatsugu Kobayashi
I am struggling to come up with an efficient vectorized way to convert
20Kx20K covariance matrix to a Euclidian distance matrix as a surrogate for
On 10/31/13 23:14, Takatsugu Kobayashi wrote:
Hi RUsers,
I am struggling to come up with an efficient vectorized way to convert
20Kx20K covariance matrix to a Euclidian distance matrix as a surrogate for
dissimilarity matrix. Hopefully I can apply multidimensional scaling for
mapping these 20K
Thanks all.
I will get real and try to reduce the size of covariance matrix.
Taka
On Fri, Nov 1, 2013 at 8:01 AM, Rolf Turner r.tur...@auckland.ac.nz wrote:
On 10/31/13 23:14, Takatsugu Kobayashi wrote:
Hi RUsers,
I am struggling to come up with an efficient vectorized way to convert
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