2005 NonNegativeTensorFactorizationw

• (Shashua & Hazan, 2005) ⇒ Amnon Shashua, and Tamir Hazan. (2005). “Non-negative Tensor Factorization with Applications to Statistics and Computer Vision.” In: Proceedings of the 22nd International Conference on Machine learning. ISBN:1-59593-180-5 doi:10.1145/1102351.1102451

Subject Headings: Tensor Factorization Algorithm, Non-Negative Matrix Factorization.

Cited By

• http://scholar.google.com/scholar?q=%222005%22+Non-negative+Tensor+Factorization+with+Applications+to+Statistics+and+Computer+Vision
• http://dl.acm.org/citation.cfm?id=1102351.1102451&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 NonNegativeTensorFactorizationw	Amnon Shashua Tamir Hazan			Non-negative Tensor Factorization with Applications to Statistics and Computer Vision				10.1145/1102351.1102451		2005