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• W2144920235 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." @default.
• W2144920235 created "2016-06-24" @default.
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• W2144920235 date "2005-01-01" @default.
• W2144920235 modified "2023-09-25" @default.
• W2144920235 title "Non-negative tensor factorization with applications to statistics and computer vision" @default.
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• W2144920235 doi "https://doi.org/10.1145/1102351.1102451" @default.
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