Switching Non-negative Matrix Factorization with Partial Distance Strategy for Incomplete Data

Abstract

Non-negative matrix factorization (NMF) is a basic method for analyzing the intrinsic structure of such non-negative matrices as environmental observation data, but cannot work well when datasets include incomplete data subsets drawn from different generative schemes. This paper proposes a novel switching NMF algorithm with partial distance strategy, which simultaneously achieves construction of cluster-wise submodels and handling of missing values.