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

Authors:
Amnon Shashua;Tamir Hazan
Affiliations:
The Hebrew University of Jerusalem, Jerusalem, Israel;The Hebrew University of Jerusalem, Jerusalem, Israel
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

Citing 9
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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.