Efficient Document Clustering via Online Nonnegative Matrix Factorizations

Abstract

In recent years, Nonnegative Matrix Factorization (NMF) has received considerable interest from the data mining and information retrieval fields. NMF has been successfully applied in document clustering, image representation, and other domains. This study proposes an online NMF (ONMF) algorithm to efficiently handle very large-scale and/or streaming datasets. Unlike conventional NMF solutions which require the entire data matrix to reside in the memory, our ONMF algorithm proceeds with one data point or one chunk of data points at a time. Experiments with one-pass and multi-pass ONMF on real datasets are presented.