%0 Generic %1 Gonzalez05acceleratingthe %A Gonzalez, Edward F. %A Zhang, Yin %D 2005 %K NMF optimization %T Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3585 %X Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung 7. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessarily converge to local minima. 1

@misc{Gonzalez05acceleratingthe, abstract = {Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung [7]. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessarily converge to local minima. 1}, added-at = {2018-04-02T05:59:40.000+0200}, author = {Gonzalez, Edward F. and Zhang, Yin}, biburl = {https://www.bibsonomy.org/bibtex/2cdcb29eb979b0989f9d045ea797ef60b/shabbychef}, description = {Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization}, interhash = {3467712b625556a8574d2ca91e2d1063}, intrahash = {cdcb29eb979b0989f9d045ea797ef60b}, keywords = {NMF optimization}, timestamp = {2018-04-02T05:59:40.000+0200}, title = {Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.3585}, year = 2005 }