Matrix Factorization and Latent Semantic Indexing Low-Rank Approximation Reduced svd If we retain only k singular values, and set the rest to 0, then we dont need the matrix parts in brown Then∑ is kXK,UisM×k, V is kXN, and Ak is MXN This is referred to as the reduced svd It is the convenient(space-saving and usual form for computational applications It's what Matlab gives you **** *** ***Matrix Factorization and Latent Semantic Indexing 21 ▪ If we retain only k singular values, and set the rest to 0, then we don’t need the matrix parts in brown ▪ Then Σ is k×k, U is M×k, V T is k×N, and Ak is M×N ▪ This is referred to as the reduced SVD ▪ It is the convenient (space-saving) and usual form for computational applications ▪ It’s what Matlab gives you Reduced SVD k Low-Rank Approximation