Abstract:
In statistical practice, rectangular tables of numeric data are commonplace, and are often analyzed using dimension reduction methods like the singular value decomposition (SVD) and its close cousin, principal component analysis (PCA). This analysis produces score and loading matrices representing the rows and the columns of the original table and these matrices may be used for both prediction purposes and to gain structural understanding of the data. In some tables, the data entries are necessarily non-negative (apart, perhaps, from some small random noise), and so the matrix factors meant to represent them should arguably also contain only non-negative elements. This thinking, and the desire for parsimony, underlies such techniques as rotating factors in a search for “simple structure.” These attempts to transform score or loading matrices of mixed sign into nonnegative, parsimonious forms are however indirect and at best imperfect. The recent development of non-negative matrix factorization, or NMF, is an attractive alternative. Rather than attempt to transform a loading or score matrix of mixed signs into one with only non-negative elements, it directly seeks matrix factors containing only non-negative elements. The resulting factorization often leads to substantial improvements in interpretability of the factors. We illustrate this potential by synthetic examples and a real data set. The question of exactly when NMF is effective is not fully resolved, but some indicators of its domain of success are given. It is pointed out that the NMF factors can be used in much the same way as those coming from PCA for such tasks as ordination, clustering and prediction.
Keywords:
Principal component analysis, PCA, Singular value decomposition, SVD, Nonnegative matrix factorization, NMF, latent dimensions.
tr185.pdf