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ICML
2005
IEEE

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

10 years 11 months ago
Non-negative tensor factorization with applications to statistics and computer vision
We derive algorithms for finding a nonnegative 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.
Amnon Shashua, Tamir Hazan
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Amnon Shashua, Tamir Hazan
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