Dimensionality Reduction

Question 1

What’s SVD?

Answer 1

Any rectangular matrix can be decomposed into USV, S is diagonal with singular values, U is rectangular with singular vectors as columns, V is rectangular with singular vectors as rows. U and V are orthogonal. Geometrically it’s rotation, scale, rotation.