Results 281 to 290 of about 32,711 (308)

Nonnegative Discriminant Matrix Factorization

IEEE Transactions on Circuits and Systems for Video Technology, 2017
Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification.
Yuwu Lu, Zhihui Lai, Yong Xu, Xuelong Li, David Zhang, Chun Yuan   +5 more
openaire   +2 more sources

Elastic Nonnegative Matrix Factorization

2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018
Nonnegative Matrix Factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process.
Peter Ballen, Sudipto Guha
openaire   +1 more source

Elastic nonnegative matrix factorization

Pattern Recognition, 2019
Abstract Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust l2,1-norm to measure the quality of factorization, given the assumption of i.i.d Gaussian noise model and i.i.d Laplacian noise model, respectively.
He Xiong, Deguang Kong
openaire   +1 more source

Manifold Peaks Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems
Nonnegative matrix factorization (NMF) has attracted increasing interest for its high interpretability in recent years. It is shown that the NMF is closely related to fuzzy k -means clustering, where the basis matrix represents the cluster centroids.
Xiaohua Xu, Ping He
openaire   +2 more sources

Constrained Clustering With Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems, 2016
Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available.
Xianchao, Zhang, Linlin, Zong, Xinyue, Liu, Jiebo, Luo   +3 more
openaire   +2 more sources

Virtual label constraint Nonnegative Matrix Factorization

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
This paper proposes a novel Semi-supervised Nonnegative Matrix Factorization (NMF), called Virtual Label Constraint Nonnegative Matrix Factorization (VLCNMF). The idea of the VLCNMF is to extend the NMF by incorporating a virtual label constraint into the NMF decomposition.
null Xiaobing Pei, null Changqing Chen, null Weihua Gong   +2 more
openaire   +1 more source

Graph-Based Multicentroid Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems
Nonnegative matrix factorization (NMF) is a widely recognized approach for data representation. When it comes to clustering, NMF fails to handle data points located in complex geometries, as each sample cluster is represented by a centroid. In this article, a novel multicentroid-based clustering method called graph-based multicentroid NMF (MCNMF) is ...
Chuan Ma, Yingwei Zhang, Chun-Yi Su
openaire   +2 more sources

Nonnegative Matrix Factorization

2019
Ebert, Peter, Oldouz Majidi
openaire   +2 more sources

The biofilm matrix: multitasking in a shared space

Nature Reviews Microbiology, 2022
Hans-Curt Flemming, Eric D Van Hullebusch, Thomas R Neu   +2 more
exaly  

Extracellular vesicle–matrix interactions

Nature Reviews Materials, 2023
, Jae-won Shin
exaly