What is NMF?
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Nonnegative Matrix Factorization (NMF) is a machine learning algorithm that is used to extract meaningful features from datasets. It is particularly effective in dealing with noisy data and improving the efficiency of machine learning tasks . NMF can be applied to various domains, including social management systems and neuroimaging. In complex social management systems, NMF can be used to extract multi-angle features and represent data using a hierarchical structure . In the field of neuroimaging, orthonormal projective NMF has been widely used to derive representations of the brain in health and disease, providing intuitive and well-localized factors . TL-NMF is a variant of NMF that aims to learn data representations suited to NMF by incorporating transform learning. It has been shown that TL-NMF can be replaced by a two-step approach called JD+NMF, which estimates the transform through joint-diagonalization prior to NMF computation .
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| Papers (5) | Insight |
|---|---|
| NMF stands for non-negative matrix factorization. | |
| NMF stands for Non-negative Matrix Factorization. | |
| NMF stands for Nonnegative Matrix Factorization. It is an algorithm used in machine learning to divide a large matrix into two smaller matrices, which can then be multiplied jointly to form the initial large matrix. The algorithm ensures that all values in the matrices are positive. | |
| NMF stands for Non-Negative Matrix Factorization. | |
6 Citations | NMF stands for non-negative matrix factorization. It is a topic modeling algorithm that does not require manual annotations for domain terms and extracts topics from a corpus of domain documents. |
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