Towards evolutionary nonnegative matrix factorization

Fei Wang, Hanghang Tong, Ching Yung Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in a computation and space efficient manner with the variation of the data matrix. We devise such evolutionary procedure for both asymmetric and symmetric NMF. Finally we conduct experiments on several real world data sets to demonstrate the efficacy and efficiency of eNMF.

Original languageEnglish (US)
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages501-506
Number of pages6
StatePublished - Nov 2 2011
Externally publishedYes
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: Aug 7 2011Aug 11 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
CountryUnited States
CitySan Francisco, CA
Period8/7/118/11/11

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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