learned projection

In machine learning, “projection” often refers to learned linear transformations via matrix multiplication, not geometric projection.

Matrix multiplication can map between vector spaces of different dimensions: ${\mathbb{R}}^{m\times n}\mapsto {\mathbb{R}}^{m\times p}$

Projections onto lower-dimensional subspaces are not invertible / surjective – there is a loss of information.