Incremental Learning in the Non-negative Matrix Factorization

The non-negative matrix factorization (NMF) is capable of factorizing strictly positive data into strictly positive activations and base vectors. In its standard form, the input data must be presented as a batch of data. This means the NMF is only able to represent the input space contained in this batch of data whereas it is not able to adapt to changes afterwards. In this paper we propose a method to overcome this limitation and to enable the NMF to incrementally and continously adapt to new data. The proposed algorithm is able to cover the (possibly growing) input space without putting further constraints on the algorithm. We show that using our method the NMF is able to approximate the dimensionality of a dataset and therefore is capable to determine the required number of base vectors automatically.

Authors and Affiliations

1. Honda Research Institute Europe GmbH, Carl-Legien-Strasse 30, 63073, Offenbach am Main, Germany

   Sven Rebhan & Julian Eggert

2. Darmstadt University of Technology (Control Theory and Robotics Lab), Landgraf-Georg-Strasse 4, 64283, Darmstadt, Germany

   Waqas Sharif

Editors and Affiliations

1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

   Mario Köppen

2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

   Nikola Kasabov

3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

   George Coghill

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