I am new to Singular Value Decomposition and I understand that it is a way to factorise a matrix into left singular (U), right singular(V) and a diagonal matrix (Σ, containing singular values). I am learning SVD for applying to PCA and I wanted to know the formula for mapping an arbitrary vector x (Belonging to R of D dimensions) to the principal subspace of X with the help of U,V and Σ.
A=UΣV'
Thanks.