Efficient nonnegative matrix factorization with random projections

Fei Wang, Ping Li

Research output: Contribution to conferencePaper

25 Scopus citations

Abstract

The recent years have witnessed a surge of interests in Nonnegative Matrix Factorization (NMF) in data mining and machine learning fields. Despite its elegant theory and empirical success, one of the limitations of NMF based algorithms is that it needs to store the whole data matrix in the entire process, which requires expensive storage and computation costs when the data set is large and high-dimensional. In this paper, we propose to apply the random projection techniques to accelerate the NMF process. Both theoretical analysis and experimental validations will be presented to demonstrate the effectiveness of the proposed strategy.

Original languageEnglish (US)
Pages281-292
Number of pages12
StatePublished - Dec 1 2010
Externally publishedYes
Event10th SIAM International Conference on Data Mining, SDM 2010 - Columbus, OH, United States
Duration: Apr 29 2010May 1 2010

Other

Other10th SIAM International Conference on Data Mining, SDM 2010
CountryUnited States
CityColumbus, OH
Period4/29/105/1/10

All Science Journal Classification (ASJC) codes

  • Software

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    Wang, F., & Li, P. (2010). Efficient nonnegative matrix factorization with random projections. 281-292. Paper presented at 10th SIAM International Conference on Data Mining, SDM 2010, Columbus, OH, United States.