Which latent variable model is better to find hidden variable?

Currently, I am exploring the concept of latent variable for regression type datasets.

I have gone through literature of few of the methods and models used in finding latent variable, like: EM algorithms, Partial least square regression, Latent semantic analysis, Mixed Effect models (linear-nonlinear), HMM, and there are many more!!

For Example:

volume DataFrame head is 
    length     width      volume
0  1.395702  4.822958    40.821677
1  5.761620  9.912682   242.571731
2  3.444930  2.111199    18.904144
3  6.236642  7.609429   425.838818
4  7.270517  1.106117    39.883937

In the above mentioned dataset, height, type-of-object, units and other parameters are missing. As mentioned by people previously, it is difficult to regenerate them as such but there is a possibility of acknowledging them using latent variable.

Hence, I would like to ask which method or model will be more suited for such kind of dataset?

Thanks!!