Clustering analysis is an effective method to discover and identify tumor classes. So, this paper proposes a Fuzzy C-Means clustering (FCM) algorithm based on Non-negative matrix factorization (NMF). Firstly, gene expression profiling (GEP) is simply processed through mean and variance of gene expression, which can then be mapped into a low dimensional space by NMF method. Finally, for discover...

Knee joint stability is crucial in human daily activity; however, it is unclear how the central nervous system uses muscle redundancies to create stability. Muscle synergy may be a means by which this control is simplified. These synergies can be extracted using non-negative matrix factorization (NMF). The outputs of this method are finite basis synergies and weight coefficients. We address two...

Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multilayer perceptron trained with a Wasserstein-...

An unsupervised classification method provides the interpretation, feature extraction and endmember estimation for the remote sensing image data without any prior knowledge of the ground truth. We explore such method and construct an algorithm based on the non-negative matrix factorization (NMF). The use of the NMF is to match the non-negative property in sensing spectrum data. The data dimensi...

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ improves the found decompositions. Additionally, ...

The non-negative matrix factorization (NMF) determines a lower rank approximation of a matrix where an interger "!$# is given and nonnegativity is imposed on all components of the factors % & (' and % )'* ( . The NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. In applications where the components of the data are necessaril...

Non-negativity matrix factorization (NMF) and its variants have been explored in the last decade and are still attractive due to its ability of extracting non-negative basis images. However, most existing NMF based methods are not ready for encoding higher-order data information. One reason is that they do not directly/explicitly model structured data information during learning, and therefore ...

Face recognition is a challenging issue in the field of multi-science, the main contents of the research is how to make computer have the ability of face recognition face recognition technology involved in a lot, which is a key feature extraction and classification method, this paper focuses on the study of related theory. Non-negative matrix factorization trapped MF) algorithm and local non-ne...

In this paper, a multi-focus image fusion method based on the weighted non-negative matrix factorization and focal point analysis is presented to improve image fusion method based on non-negative matrix factorization. The relationship among the point spread function, the optical system and imaging processing is analyzed in the method, then a weighted matrix U is built by calculating the differe...

Properties of a novel algorithm called non-negative matrix factorization (NMF), are studied. NMF can discover substructures and can provide estimations about the presence or the absence of those, being attractive for completion of missing information. We have studied the working and learning capabilities of NMF networks. Performance was improved by adding sparse code shrinkage (SCS) algorithm t...