MHB What does the notation $||A-B||_{2,a}$ represent in terms of matrix norms?

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The notation $||A-B||_{2,a}$ represents the element-wise (or Frobenius) norm of the 2D convolution of matrices A and B with a Gaussian kernel of standard deviation a. The index 2 indicates the use of the $\ell_2$ norm, while the parameter a specifies the characteristics of the Gaussian kernel applied during the convolution. This norm is defined similarly to vector norms, but it incorporates the convolution process. Understanding this notation is crucial for applications involving matrix operations and Gaussian smoothing. The discussion clarifies the meaning and application of this specific matrix norm notation.
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Hello everyone!

I came across, in a reading, an unfamiliar norm notation: $||A-B||_{2,a}$ where $a$ is the standard deviation of a Gaussian kernel. Now I know that the index 2 represents the $\ell _2$ norm, but what about the $a$?

Moreover, is the matrix norm defnied in a similar way to the vector norm?Any help is apptreciated!
 
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OhMyMarkov said:
Hello everyone!

I came across, in a reading, an unfamiliar norm notation: $||A-B||_{2,a}$ where $a$ is the standard deviation of a Gaussian kernel. Now I know that the index 2 represents the $\ell _2$ norm, but what about the $a$?

Moreover, is the matrix norm defnied in a similar way to the vector norm?Any help is apptreciated!

Hi OhMyMarkov, :)

The definition of the matrix norm and the notation you are taking about are explained here.

Kind Regards,
Sudharaka.
 
Thank you, Sudharaka,

I may need to point this out in case someone else comes across it in the future: this notation means the Element-wise (or Frobenius I guess) norm of the 2D-convolution of A with a Gaussian kernel of standard deviation a.
 

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