How is singular value decomposition used in Dimensionality Reduction




SVD is one of the techniques for dimensionality reduction.Like in PCA we can take some principal components which explain maximum variance in the data how is SVD actually used.
Also when do we use SVD,since there are other ways to do dimension reduction,which cases warrant use of SVD.
Can somebody please help me with these questions?