Results 61 to 70 of about 15,492 (160)

Locally Constrained Non-Negative Matrix Factorization for Data Clustering

IEEE Access
Semi-supervised non-negative matrix factorization (NMF) has been widely used for clustering high-dimensional data, because a small amount of label information can effectively improve clustering performance. Nevertheless, most existing semi-supervised NMF
Xuzhu Shen, Jie Li
doaj   +1 more source

Robust Hierarchical Learning for Non-Negative Matrix Factorization With Outliers

IEEE Access, 2019
Desirable properties of extensions of non-negative matrix factorization (NMF) include robustness in the presence of noises and outliers, ease of implementation, the guarantee of convergence, operation in an automatic fashion that trades off the balance ...
Yinan Li, Meng Sun, Hugo Van Hamme, Xiongwei Zhang, Jibin Yang   +4 more
doaj   +1 more source

Biclustering of gene expression data by non-smooth non-negative matrix factorization

BMC Bioinformatics, 2006
Background The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions.
Carazo Jose M, Tirado F, Pascual-Marqui Roberto D, Carmona-Saez Pedro, Pascual-Montano Alberto   +4 more
doaj   +1 more source

Towards a Fairer Non-negative Matrix Factorization

CoRR
There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on ``fair" PCA, here we consider the
Lara Kassab, Erin George, Deanna Needell, Haowen Geng, Nika Jafar Nia, Aoxi Li   +5 more
openaire   +2 more sources

Non-negative matrix factorization based single-channel source-separation of passive underwater acoustic signals in deep sea [PDF]

JASA Express Letters
We use non-negative matrix factorization for source separation on ultra-low frequency passive-acoustic data from a single-channel recording acquired in deep sea.
Jean Lecoulant, Abdel-Ouahab Boudraa, Samuel Pinson   +2 more
doaj   +1 more source

Non-negative matrix factorization with sparseness constraints [PDF]

CoRR, 2004
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.
openaire   +3 more sources

Improving drug repositioning accuracy using non-negative matrix tri-factorization

Scientific Reports
Drug repositioning is a transformative approach in drug discovery, offering a pathway to repurpose existing drugs for new therapeutic uses. In this study, we introduce the IDDNMTF model designed to predict drug repositioning opportunities with greater ...
Qingmei Li, Yangyang Wang, Jihan Wang, Congzhe Zhao   +3 more
doaj   +1 more source

Enhanced deep non-negative matrix factorization for multi-view clustering

Scientific African
Multi-view clustering (MVC) seeks to group similar samples into the same clusters by exploiting complementary and consistent information between different views.
Laishui Lv, Dalal Bardou, Heng Wu, Ting Zhang, Zijun Zheng, Ye Zhou, Yinyin Wang   +6 more
doaj   +1 more source

Non-negative matrix factorization for medical imaging [PDF]

, 2018
A non-negative matrix factorization approach to dimensionality reduction is proposed to aid classification of images. The original images can be stored as lower-dimensional columns of a matrix that hold degrees of belonging to feature components, so they can be used in the training phase of the classification at lower runtime and without loss in ...
Miguel A. Atencia Ruiz, Ruxandra Stoean
openaire   +1 more source

Robust Non-Negative Matrix Tri-Factorization with Dual Hyper-Graph Regularization

Big Data Mining and Analytics
Non-negative Matrix Factorization (NMF) has been an ideal tool for machine learning. Non-negative Matrix Tri-Factorization (NMTF) is a generalization of NMF that incorporates a third non-negative factorization matrix, and has shown impressive clustering ...
Jiyang Yu, Hangjun Che, Man-Fai Leung, Cheng Liu, Wenhui Wu, Zheng Yan   +5 more
doaj   +1 more source