Nonnegative decomposition of functional count data.

ABSTRACT: We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of can

SUBMITTER: Backenroth D 

PROVIDER: S-EPMC7375931 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature