Results 1 to 10 of about 2,151 (154)
Collaborative filtering based on nonnegative/binary matrix factorization [PDF]
Frontiers in Big DataCollaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui +5 more
doaj +2 more sources
Biometrics, 2011 In Western countries where food supply is satisfactory, consumers organize their diets around a large combination of foods. It is the purpose of this article to examine how recent nonnegative matrix factorization (NMF) techniques can be applied to food consumption data to understand these combinations.
Philippe Verger
exaly +7 more sources
Linking Cortical Morphometry in Self-Limited Epilepsy With Centrotemporal Spikes to Cognition, Function, and Molecular Architecture. [PDF]
CNS Neurosci TherThis study provides a novel framework modeling neurodevelopment in selfâlimited epilepsy with centrotemporal spikes. We identify eight biologically interpretable brain structural deviation patterns, which are linked to clinical phenotypes and molecular profiles involving cognition and mitochondrial function. This integration elucidates multidimensional
Yang S +6 more
europepmc +2 more sources
Robust Structured Convex Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2021 Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning. Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix.
Qing Yang +3 more
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Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
doaj +1 more source
Mathematics, 2023 NONNEGATIVE matrix factorization (NMF) is an effective technique for dimensionality reduction of high-dimensional data for tasks such as machine learning and data visualization.
Jie Li, Yaotang Li, Chaoqian Li
doaj +1 more source
Transductive Nonnegative Matrix Tri-Factorization
IEEE Access, 2020 Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.
Xiao Teng +4 more
doaj +1 more source
Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
doaj +1 more source
CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)
, 2023 Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering
Sajida Mhammedi +3 more
openaire +2 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 The fusion of high-resolution multispectral image (HrMSI) and low-resolution hyperspectral image (LrHSI) has been acknowledged as a promising method for generating a high-resolution hyperspectral image (HrHSI), which is also termed to be an essential part for precise recognition and cataloguing of the underlying materials.
Abdolraheem Khader +2 more
openaire +2 more sources