Non-negative tensor factorization with applications to statistics and computer vision

Amnon Shashua*, Tamir Hazan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

356 Scopus citations

Abstract

We derive algorithms for finding a non-negative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii) sparse image coding in computer vision, and (iii) model selection problems. We derive a "direct" positive-preserving gradient descent algorithm and an alternating scheme based on repeated multiple rank-1 problems.

Original languageAmerican English
Title of host publicationICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
EditorsL. Raedt, S. Wrobel
Pages793-800
Number of pages8
StatePublished - 2005
EventICML 2005: 22nd International Conference on Machine Learning - Bonn, Germany
Duration: 7 Aug 200511 Aug 2005

Publication series

NameICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Conference

ConferenceICML 2005: 22nd International Conference on Machine Learning
Country/TerritoryGermany
CityBonn
Period7/08/0511/08/05

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