Calculating Mean Square Error with Pseudo Inverse Approach

In summary, the conversation discussed finding the mean square error using the pseudo inverse approach. The formula Rhat = A[(A'A)^-1 ]A' R was mentioned, which results in an 11x11 matrix. The question of whether a 8x11 matrix should be obtained was raised, but the correct answer is no. The conversation also touched on finding the most optimum vector F, but no solution was provided. Lastly, the equation AF=R was brought up, but it was determined that it is not overdetermined and does not make sense.
  • #1
nikki92
40
0
Find the mean square error using the pseudo inverse approach.

I am given a 11X9 matrix A, a 11X1 vector F and R = 11X11 diagonal matrix

so Rhat = A[(A'A)^-1 ]A' R . Then I get a 11X11 matrix. Shouldn't I get getting a 8X11 matrix How do I get the most optimum vector F?
 
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  • #2
nikki92 said:
Find the mean square error using the pseudo inverse approach.

I am given a 11X9 matrix A, a 11X1 vector F and R = 11X11 diagonal matrix

so Rhat = A[(A'A)^-1 ]A' R . Then I get a 11X11 matrix.

Correct.

Shouldn't I get getting a 8X11 matrix?

No.

How do I get the most optimum vector F?

I don't know.
 
  • #3
I have a linear question A*F =R where R is the diagonal 11 x 11 matrix A is 9X11 and F is 9X1. This system is over determined. I am confused on how to get the values of F .

I get that F =[(A'A)^-1 ]A' R which gives me a 9 x 11 matrix which does not make sense .
 
  • #4
nikki92 said:
I have a linear question A*F =R where R is the diagonal 11 x 11 matrix A is 9X11 and F is 9X1. This system is over determined. I am confused on how to get the values of F .

I get that F =[(A'A)^-1 ]A' R which gives me a 9 x 11 matrix which does not make sense .
Your equation AF=R is not overdetermined. It doesn't even make sense. There is no such thing as the product of a 9x11 matrix with a 9x1 matrix. If your A was 11x9, then the product AF would be defined, but it's dimensionality would be 11x1, not 11x11.
 

Related to Calculating Mean Square Error with Pseudo Inverse Approach

1. What is the Pseudo Inverse Approach for calculating Mean Square Error?

The Pseudo Inverse Approach is a mathematical method used to calculate the Mean Square Error (MSE) of a dataset. It involves finding the inverse of the data matrix and using it to calculate the MSE.

2. How is Pseudo Inverse Approach different from other methods of calculating MSE?

Pseudo Inverse Approach is different from other methods because it takes into account the error or noise in the data and minimizes it by finding the inverse of the data matrix.

3. What are the advantages of using Pseudo Inverse Approach for calculating MSE?

One of the main advantages of using Pseudo Inverse Approach is that it can handle datasets with a high number of variables or features. It also takes into account the error in the data, making it more accurate than other methods.

4. Can Pseudo Inverse Approach be used for any type of dataset?

Yes, Pseudo Inverse Approach can be used for any type of dataset, including numerical and categorical data. However, it is more commonly used for numerical data.

5. Are there any limitations to using Pseudo Inverse Approach for calculating MSE?

One limitation of Pseudo Inverse Approach is that it can be computationally expensive for large datasets. It also assumes that the data is linearly related, which may not always be the case. Additionally, it may not work well with datasets that have a high degree of collinearity.

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