Difference between revisions of "Reviews on curse of dimensionality"

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(New page: ---- =Questions and Comments= * Tian's review: It's a great slecture, the explanation is in detail and the logic between the steps is quite clear. Also, the MATLAB example provides me w...)

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Lee714 (Talk | contribs)

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=Questions and Comments=

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* Tian's review: It's a great slecture, the explanation is in detail and the logic between the steps is quite clear. Also, the MATLAB example provides me with a certain level of intuition of how the whitening process works. This slecture ~~greatly helps me to understand the transformation between different Gaussian distributions, in both mathematical sense and practical sense.~~

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This slecture will be reviewed by Soonam Lee

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* Tian's comment 1: As we can see, the whited data does not have exactly zero mean and an identity covariance matrix, there are some tiny errors, is there a way that we can characterize how good the whiting process is from those errors? I guess the error should be linear to the difference between the largest and smallest eigenvalues of the covariance matrix. This could be a further topic that readers want to explore.

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* Tian's comment 2: The real-world data that we process is usually not exactly multivariate Gaussian distribution, if the original data was not Gaussian distribution, or even very far away from a Gaussian distribution, can we still use this technique and how good the result would be? This could be ~~an interesting question that readers could further explore.~~

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Difference between revisions of "Reviews on curse of dimensionality" - Rhea

Revision as of 18:26, 24 April 2014

Questions and Comments

Alumni Liaison

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 =Questions and Comments=
-* Tian's review: It's a great slecture, the explanation is in detail and the logic between the steps is quite clear. Also, the MATLAB example provides me with a certain level of intuition of how the whitening process works. This slecture greatly helps me to understand the transformation between different Gaussian distributions, in both mathematical sense and practical sense.
+This slecture will be reviewed by Soonam Lee
-* Tian's comment 1: As we can see, the whited data does not have exactly zero mean and an identity covariance matrix, there are some tiny errors, is there a way that we can characterize how good the whiting process is from those errors? I guess the error should be linear to the difference between the largest and smallest eigenvalues of the covariance matrix. This could be a further topic that readers want to explore.
-* Tian's comment 2: The real-world data that we process is usually not exactly multivariate Gaussian distribution, if the original data was not Gaussian distribution, or even very far away from a Gaussian distribution, can we still use this technique and how good the result would be? This could be an interesting question that readers could further explore.
-* Questions and comments
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