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Distribution AgreementDistribution Agreement • April 5th, 2018
Using a truncated version of the singular value decomposition can help both with the cost of the algorithm and the issue of amplifying noise. Instead of computing a full singular value decomposition to exactly represent K, we can compute only the first k singular values and vectors, where k ≤ N . This permits a compact representation where the matrices U and V are size N × k and the matrix Σ is size k × k. The storage cost is then reduced to O(Nk) entries, and the computation time is reduced to O(N 2k). This factorization does not have an exact inverse, but we can compute
