done for each class separately as I understand it it puts a lot of weight on the first singular vector and then it pushes down the contributions of the other singular vector which means that the data set that is analyzed is is concentrated in two fewer directions of variance this is layer one and here is layer three means so you see it happens in both that the manifold makes up compared to the baseline model does this so now you might ask what is manifold mix-up it's actually a pretty pretty simple concept right here is another