Table 2.

Summary of the advantages of SVD and NMF matrix factorization methods. The advantages of one method complement the disadvantages of the other (Langville et al. 2006). For example, the NMF optimization problem is nonconvex with local minima resulting in solutions that depend on the initialization of the algorithm.

SVD advantages NMF advantages
Optimal rank r approximation Results are nonnegative
Fast to compute Results are sparse
Unique Sparsity and nonnegativity lead to improved interpretability

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