The result of scaled an non-scaled data will be different because the
regularization will have a different effect.
On 06/05/2015 03:10 AM, Yury Zhauniarovich wrote:
Thank you all! However, what Sturla wrote is now out of my understanding.
One more question. It seems also to me that Naive Bayes classifiers
also do not need data to be scaled. Am I correct?
Best Regards,
Yury Zhauniarovich
On 4 June 2015 at 20:55, Sturla Molden <sturla.mol...@gmail.com
<mailto:sturla.mol...@gmail.com>> wrote:
On 04/06/15 20:38, Sturla Molden wrote:
> Component-wise EM (aka CEM2) is a better way of avoiding the
singularity
> disease, though.
The traditional EM for a GMM proceeds like this:
while True:
global_estep(clusters)
for c in clusters:
mstep(c)
This is inherently unstable. Several clusters can become
near-singular in the M-step before there is an E-step
to redistribute the weights. You can get a "cascade of
singularities" where the whole GMM basically dies. Even
if you bias the diagonal of the covariance you still
have the basic algorithmic problem.
CEM2 proceeds like this:
while True:
for c in clusters:
estep(c)
mstep(c)
This improves stability enormously. When a cluster becomes
singular, the memberships are immediately redistributed.
Therefore you will not get a "cascade of singularities"
where the whole GMM basically dies.
Sturla
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