P. Paatero and U. Tapper, Least squares formulation of robust nonnegative factor analysis, Chemomet. Intell. Lab. Systems, 37(1997), 23-35. Home/Search Document Not in Database Summary Related Articles Check |
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Algorithms for Non-negative Matrix Factorization - Lee, Seung (2001) (54 citations) (Correct)
....V WH , we first need to define cost functions that quantify the quality of the approximation. Such a cost function can be constructed using some measure of distance between two non negative matrices A and B. One useful measure is simply the square of the Euclidean distance between A and B [13], jjA Bjj (A ij B ij ) 2) This is lower bounded by zero, and clearly vanishes if and only if A = B. Another useful measure is D(AjjB) A ij log A ij B ij A ij B ij (3) Like the Euclidean distance this is also lower bounded by zero, and vanishes if and only if A = B. But it ....
Paatero, P & Tapper, U (1997). Least squares formulation of robust non-negative factor analysis. Chemometr. Intell. Lab. 37, 23--35.
A Two-Layer Sparse Coding Model Learns Simple and Complex.. - Hyvärinen, Hoyer (2001) (Correct)
.... models (Simoncelli and Schwartz, 1999) A more general signal processing framework with this kind of dependencies was proposed in (Hyvarinen et al. 2001) Some relation may also be found with the models of positive factor analysis or non negative matrix factorization (Lee and Seung, 1999; Paatero, 1997). 5.3 Extensions and critique Extending this modelling approach to non spatial properties of complex cells and topography may be possible by adding these properties to the input data. The basic ICA model has had some success in modelling properties of simple cells related to motion (van Hateren ....
Paatero, P. (1997). Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems, 37:23--35.
Algorithms for Non-negative Matrix Factorization - Lee, Seung (2000) (54 citations) (Correct)
....V WH , we first need to define cost functions that quantifies the quality of the approximation. Such a cost function can be constructed using some measure of distance between two non negative matrices A and B. One useful measure is simply the square of the Euclidean distance between A and B [12], jjA Bjj 2 = X ij (A ij B ij ) 2 (2) This is lower bounded by zero, and clearly vanishes if and only if A = B. Another useful measure is D(AjjB) X ij A ij log A ij B ij A ij B ij (3) Like the Euclidean distance this is also lower bounded by zero, and vanishes if and only ....
Paatero, P & Tapper, U (1997). Least squares formulation of robust non-negative factor analysis. Chemometr. Intell. Lab. 37, 23--35.
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P. Paatero and U. Tapper, Least squares formulation of robust nonnegative factor analysis, Chemomet. Intell. Lab. Systems, 37(1997), 23-35.
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P. Paatero. Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems, 37, 1997.
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P. Paatero and U. Tapper, Least squares formulation of robust nonnegative factor analysis, Chemomet. Intell. Lab. Systems, 37(1997), 23-35.
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P. Paatero, Least squares formulation of robust nonnegative factor analysis, Chemomet. Intell. Lab. Systems, 37(1997), 23-35.
Non-Negative Matrix Factorization for Polyphonic Music.. - Smaragdis, Brown (2003) (4 citations) (Correct)
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Paatero, P. "Least squares formulation of robust nonnegative factor analysis". In Chemometrics and Intelligent Laboratory Systems 37, pp23-35, (1997).
Non-Negative Matrix Factorization for Polyphonic Music.. - Smaragdis, Brown (2003) (4 citations) (Correct)
No context found.
Paatero, P. "Least squares formulation of robust nonnegative factor analysis". In Chemometrics and Intelligent Laboratory Systems 37, pp23-35, (1997).
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