Online Nonnegative Matrix Factorization with General Divergences

Renbo Zhao, Vincent Tan, Huan Xu
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:37-45, 2017.

Abstract

We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences. The online nature of our algorithms makes them particularly amenable to large-scale data. We prove that the sequence of learned dictionaries converges almost surely to the set of critical points of the expected loss function. Experimental results demonstrate the computational efficiency and outstanding performances of our algorithms on several real-life applications, including topic modeling, document clustering and foreground-background separation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-zhao17a, title = {{Online Nonnegative Matrix Factorization with General Divergences}}, author = {Zhao, Renbo and Tan, Vincent and Xu, Huan}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {37--45}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/zhao17a/zhao17a.pdf}, url = {https://proceedings.mlr.press/v54/zhao17a.html}, abstract = {We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences. The online nature of our algorithms makes them particularly amenable to large-scale data. We prove that the sequence of learned dictionaries converges almost surely to the set of critical points of the expected loss function. Experimental results demonstrate the computational efficiency and outstanding performances of our algorithms on several real-life applications, including topic modeling, document clustering and foreground-background separation.} }
Endnote
%0 Conference Paper %T Online Nonnegative Matrix Factorization with General Divergences %A Renbo Zhao %A Vincent Tan %A Huan Xu %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-zhao17a %I PMLR %P 37--45 %U https://proceedings.mlr.press/v54/zhao17a.html %V 54 %X We develop a unified and systematic framework for performing online nonnegative matrix factorization under a wide variety of important divergences. The online nature of our algorithms makes them particularly amenable to large-scale data. We prove that the sequence of learned dictionaries converges almost surely to the set of critical points of the expected loss function. Experimental results demonstrate the computational efficiency and outstanding performances of our algorithms on several real-life applications, including topic modeling, document clustering and foreground-background separation.
APA
Zhao, R., Tan, V. & Xu, H.. (2017). Online Nonnegative Matrix Factorization with General Divergences. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:37-45 Available from https://proceedings.mlr.press/v54/zhao17a.html.

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