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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. | * 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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* Tian's comments 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 comments 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 comments 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. | * Tian's comments 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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* Questions and comments | * Questions and comments | ||
Revision as of 06:02, 13 April 2014
Talk: Whitening and Coloring Transforms for Multivariate Gaussian Random Variables
A slecture by ECE student Maliha Hossain on
Loosely based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
This is the talk page for the sLecture notes on Whitening and Coloring Transforms for Multivariate Gaussian Random Variables. Please leave me a comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.
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.
- Tian's comments 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 comments 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
