So I'm not ML guru or anything but what I learned was that if you have m features on n samples you want n > m to prevent over-fitting, no?

Also, with so few samples, how do you do your hyperparameter tuning and validation?

I mean you could eliminate certain features in isolation but that doesn't capture dependent features. And how would you do dimensionality reduction?