Results 71 to 80 of about 7,904 (176)
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
, 2015 Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing.
Honeine, Paul, Zhu, Fei
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The Journal of Engineering, Volume 2026, Issue 1, January/December 2026.ABSTRACT Smart cities represent transformative urban ecosystems, leveraging advanced technologies to address challenges posed by rapid urbanisation, energy efficiency demands, and sustainability goals. Despite extensive research, a comprehensive and integrated literature analysis combining AI‐based communication, energy management, cybersecurity, and ...
Maaz Tahir Malik +5 more
wiley +1 more source
A multilevel approach for nonnegative matrix factorization [PDF]
Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing ...
GILLIS, Nicolas, GLINEUR, François
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Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization [PDF]
, 2013 Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and
AbdulJabbar, Mustafa, Wang, Jing-Yan
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Human Mutation, Volume 2026, Issue 1, 2026.Translating high‐resolution multiomics data into clinically actionable biomarkers is critical for overcoming therapeutic resistance and tumor heterogeneity in prostate adenocarcinoma (PRAD). To decode the complex immunosuppressive tumor microenvironment (TME) and identify robust prognostic targets, we developed a systematic biomarker discovery pipeline
Changcheng Luo +5 more
wiley +1 more source
International Journal of Applied Mathematics and Computer Science, 2014 Nonnegative Matrix Factorization (NMF) is an important tool in data spectral analysis. However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation matrix is not unique.
Zdunek Rafał
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Human Mutation, Volume 2026, Issue 1, 2026.Background Migraine (MI), Ménière′s disease (MD), and vestibular migraine (VM) share significant clinical and pathological similarities, particularly their link to neurological dysfunction and immune cell activity, though mechanisms remain poorly understood.
Mika Pan +13 more
wiley +1 more source
Underwater acoustic target signal enhancement algorithm optimized by feature preservation and noise update [PDF]
Zhihui kongzhi yu fangzhenThe enhancement effect of the classic Nonnegative Matrix Factorization (NMF) applied to underwater acoustic target signal is unsatisfactory for the feature overlap of underwater acoustic target signal and the variability of ocean underwater acoustic ...
XIAO Haixia, CUI Shuangyue, LI Dawei, SUN Mingming, LIU Xianzhong, YANG Zhenxin
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Advanced Science, Volume 12, Issue 47, December 18, 2025.The origin and functional heterogeneity of pericytes in glioblastoma (GBM) remain unclear. This study identifies tumor‐originated pericytes (T‐PCs) and normal‐originated pericytes as two distinctive populations in human GBM using single‐cell RNA‐sequencing.
Cuiying Chu +16 more
wiley +1 more source
Approximate Nonnegative Matrix Factorization via Alternating Minimization
, 2004 In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix $V \in \R_+^{m\times n}$ find, for assigned $k$, nonnegative matrices $W\in\R_+^{m\times k}$ and $H\in\R_+^{k\times n}$ such that $V ...
Finesso, Lorenzo, Spreij, Peter
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