text: Title: maxvol adjustment
  Body: #### Is your feature request related to a problem? Please describe.
  I think there might be an issue with the maxvol function. <https://github.com/tensorly/tensorly/blob/main/tensorly/contrib/decomposition/_tt_cross.py>
  You referred to "The Greedy approximation algorithm for MAX-VOL" in the paper "Ali Çivril, Malik Magdon-Ismail: On selecting a maximum volume sub-matrix of a matrix and related problems."
  
  In that paper, they mention "Remove the projection of v from every element of A." When I read this part, I interpreted it as calculating an orthogonal vector to the vector 𝑣 with the largest norm.
   However, in your code, it doesn't seem to calculate the orthogonal vector. Instead, it uses something that looks strange to me.
  
  Maybe I am misunderstanding your code or the paper. 
  Could you clarify whether I am wrong or if there is indeed an issue with the code?
  
  #### Describe alternatives you've considered
  ![image](https://github.com/user-attachments/assets/16ce1abc-bd82-40e1-8961-db90f6e6a3c0)
  This is your code
  
  ```python
  #Find the row of max norm"
  max_row_idx = np.argmax(rows_norms, axis=0)
  max_row = A[rest_row_idx[max_row_idx], :]
  
  projection = np.dot(A_new, max_row.T)
  projection = projection / np.sum(max_row**2)
          
  #calculate orthogonal vector"
  A_new = A_new - np.outer(projection, max_row)
  ```
  
  This is my code
  
  #### Additional context
  When I compared the performance between my code and yours, here are the results:
  ![image](https://github.com/user-attachments/assets/98f44e9f-6a9a-4b78-b538-07257eadc182)
  
  - The **blue line** represents my code, while the **orange line** represents your code. 
  - You can see that the volume achieved by my code is almost always larger than yours.
  - Occasionally, your code outperforms mine, but this happens in only **11% of the iterations**.
  
  The values on the graph were calculated using a randomly generated matrix.
  
  Could you clarify the cause of this discrepancy? Is there an issue with the logic in your code, or is it something I might be missing? 
  Let me know if this works!