How can I use machine learning to predict a matrix output with multiple matrices as predictors?

I want to use four matrices as predictors, with a single matrix as an output. All values in the matrices are real floating points. I've tried using element (i, j) of each predictor matrix to predict the (i, j) element of the output matrix, but (although I can get it to run) this won't work because I expect each element of the output matrix to be at least somewhat correlated to every value in all four predictor matrices. I used the Regression Learner app within the Statistics and Machine Learning Toolbox 11.6 to do this.

I've also looked into image recognition a little, but it seems that although it can handle 2D data, it works as a classifier, whereas I want a matrix of floats.

Any ideas?

[xin zhong]

Hello, have you solved this problem now? I also have the same problem now, I want to ask you for help, thank you very much!

Answer 1

[Sai Bhargav Avula]

Hi,

As you mentioned if each element depends on the same element position of the input then probably it might not work as expected. But this also dependent on what application that you are using the model for. You can look for multi target regression models for having matrix numeric outputs.

You can look at the mvregress which also within the Statistics and Machine Learning Toolbox.

Hope this helps!