This article presents a form of bi-cross-validation (BCV) for choosing the rank in outer product models, especially the singular value decomposition (SVD) and the nonnegative matrix factorization (NMF). Instead of leaving out a set of rows of the data matrix, we leave out a set of rows and a set of columns, and then predict the left out entries by low rank operations on the retained data. We prove a selfconsistency result expressing the prediction error as a residual from a low rank approximation. Random matrix theory and some empirical results suggest that smaller hold-out sets lead to more over-fitting, while larger ones are more prone to under-fitting. In simulated examples we find that a method leaving out half the rows and half the columns performs well.
Recently, Owen and Perry REF proposed a method to leave out a sub-matrix.
Bi-cross-validation of the SVD and the nonnegative matrix factorization
Annals of Applied Statistics 2009, Vol. 3, No. 2, 564-594
Mathematics