# Classification problem using features with unequal sizes

I am relatively new to Machine Learning/ Deep Learning and currently I am working on a classification problem. I have many 2D images and each of them is a cross section of a specimen showing the deformation after applying a load. The images show the edges of the specimen with the same color and the deformed edges are shown with different colors. I want to use the values of pixels as one of the feature to my classification problem. At the same time I also want to use the load value, the co-ordinates of the position of impact as features. Therefore my problem would look something like this

     Feature 1           Feature 2          Feature 3         Class
Vector of size N*1  Vector of size 3*1   Scalar value m1     Class A
Vector of size N*1  Vector of size 3*1   Scalar value m2     Class B


and so on..

I would like to know if there is a way to deal with this kind of problem or if there are any papers which focus on approaches to deal something similar to this. Any kind of help is really appreciated. If any clarification is needed from my side, do let me know. Thank you.

• The general approach would be to simply concatenate all the vectors into one long vector used as the instance. – Erwan Jul 16 at 15:01