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I am currently working on KPLS techniques as part of my thesis. I am trying to reproduce the results established in the following article: https://hal.archives-ouvertes.fr/hal-01232938/document . The article focuses in part on applying KPLS model on the Griewank function while varying the input ranges, the number of inputs and the number of learning points.
I wrote a code to test KPLS under the same conditions as those defined in the article. Here is my code :
I am using the exact same error definition as the one defined in the article. The error is computed over 5000 random test points.
Correlation function is gaussian.
Here are the results I get (array on the left are my results and array on the right the results from the article)
My fist observation is that the error depends a lot on the DOE used for learning, that is to say, if I generate a new DOE for the same case, I will not get the same results.
In case 3, you can see that I used 2 different samples, one with ESE optimization and one with center criterion which leads to very different results. Furthermore, using KPLS with 2 or 3 components is supposed to lead to very small error (according to the array on the right).
What could be the cause of such a difference ?
I also did somme tests with [-5 5] input range. Do not hesitate to ask me for more details.
Thank you in advance,
Alexandre
The text was updated successfully, but these errors were encountered:
Hi, sorry for the late answer. I can't reproduce the results of the paper with the latest version either. As you may have guessed it is not a high priority but it should definitely be investigated by pulling older versions to pin down a change responsible for this.
Hello,
I am currently working on KPLS techniques as part of my thesis. I am trying to reproduce the results established in the following article: https://hal.archives-ouvertes.fr/hal-01232938/document . The article focuses in part on applying KPLS model on the Griewank function while varying the input ranges, the number of inputs and the number of learning points.
I wrote a code to test KPLS under the same conditions as those defined in the article. Here is my code :
I am using the exact same error definition as the one defined in the article. The error is computed over 5000 random test points.
Correlation function is gaussian.
Here are the results I get (array on the left are my results and array on the right the results from the article)
My fist observation is that the error depends a lot on the DOE used for learning, that is to say, if I generate a new DOE for the same case, I will not get the same results.
In case 3, you can see that I used 2 different samples, one with ESE optimization and one with center criterion which leads to very different results. Furthermore, using KPLS with 2 or 3 components is supposed to lead to very small error (according to the array on the right).
What could be the cause of such a difference ?
I also did somme tests with [-5 5] input range. Do not hesitate to ask me for more details.
Thank you in advance,
Alexandre
The text was updated successfully, but these errors were encountered: