  Online nonnegative matrix factorization (ONMF) is a matrix factorization
technique in the online setting where data are acquired in a streaming fashion
and the matrix factors are updated each time. This enables factor analysis to
be performed concurrently with the arrival of new data samples. In this
article, we demonstrate how one can use online nonnegative matrix factorization
algorithms to learn joint dictionary atoms from an ensemble of correlated data
sets. We propose a temporal dictionary learning scheme for time-series data
sets, based on ONMF algorithms. We demonstrate our dictionary learning
technique in the application contexts of historical temperature data, video
frames, and color images.
