Mapping of categorical features into binary indicator features
Compactifying the data like this saves space in memory, but it adds false relationships that one-hot encoding doesn't.

Let's consider a categorical feature with levels A, B, C, D, that you decide to encode as 00, 01, 10, 11 respectively.

In a linear model, you only get three parameters (a constant and one for each new feature); you can fit "the right" parameters to hit A, B, and C, but then fixing those parameters determines what happens on D, and may be a very poor fit there.  (You're now assuming essentially that (B-A)+(C-A)=D-A.)

In k-NN, the distance between any two levels in the one-hot encoding is the same, 1.  In this encoding, the distance between A and B is 1, but the distance between A and D is $\sqrt{2}$.

In SVM, the set {A,D} is not separable from {B,C}.  With one-hot encoding, everything is separable.

In a tree model, using the raw categorical allows the tree to split any subset of the levels against the rest; using this binary encoding forces the tree to split (AB|CD) or (AC|BD) [missing (AD|BC)]; using one-hot encoding forces the tree to split (A|BCD) or (B|ACD) or (C|ABD) or (D|ABC).  The tree can split differently subsequently and eventually recover an arbitrary split, but a greedily built tree might never accomplish that.

Notice in particular that in these last three models, we've made A and D "more different" from each other than we might have reason to believe.  And in this small example, the catches were fairly small/few, but as the dimension increases these tend to become more exaggerated.

Now, it may still be the case that it's worth doing, but these are some things to consider.