Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation
نویسندگان
چکیده
منابع مشابه
Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation
Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering an...
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Nonnegative matrix factorisation and tri-factorisation Nonnegative matrix factorisation (NMF) and tri-factorisation (NMTF) methods decompose a given matrix R into two or three smaller matrices so that R ≈ UV T or R ≈ FSG , respectively. Schmidt, Winther and Hansen (2009) introduced a Bayesian version of nonnegative matrix factorisation (left), which we extend to matrix tri-factorisation (right)...
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Matrix factorisation models have had an explosive growth in popularity in the last decade. It has become popular due to its usefulness in clustering and missing values prediction. We review the main literature for matrix factorisation, focusing on nonnegative matrix factorisation and probabilistic approaches. We also consider several extensions: matrix tri-factorisation, Tensor factorisation, T...
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Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semiand Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundat...
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The new emerging theory of Compressive Sampling has demonstrated that by exploiting the structure of a signal, it is possible to sample a signal below the Nyquist rate and achieve perfect reconstruction. In this short note, we employ Non-negative Matrix Factorisation in the context of Compressive Sampling and propose two NMF algorithms for signal recovery—one of which utilises Iteratively Rewei...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2019
ISSN: 1041-4347,1558-2191,2326-3865
DOI: 10.1109/tkde.2018.2859223