Results 31 to 40 of about 142,276 (359)
Graph non-negative matrix factorization with alternative smoothed $$L_0$$ L 0 regularizations
Neural computing & applications (Print), 2022 Graph non-negative matrix factorization (GNMF) can discover the data’s intrinsic low-dimensional structure embedded in the high-dimensional space. So, it has superior performance for data representation and clustering.
Keyi Chen +3 more
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Parallel Non-Negative Matrix Tri-Factorization for Text Data Co-Clustering
IEEE Transactions on Knowledge and Data Engineering, 2023 As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real ...
Yufu Chen +6 more
semanticscholar +1 more source
Mathematics, 2023 Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix ...
Minghua Wan, Mingxiu Cai, Guowei Yang
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A non-convex optimization framework for large-scale low-rank matrix factorization
Machine Learning with Applications, 2022 Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in
Sajad Fathi Hafshejani +3 more
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Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data
Scientific Reports, 2022 Human activities follow daily, weekly, and seasonal rhythms. The emergence of these rhythms is related to physiology and natural cycles as well as social constructs. The human body and its biological functions undergo near 24-h rhythms (circadian rhythms)
Talayeh Aledavood +3 more
semanticscholar +1 more source
Initialization for non-negative matrix factorization: a comprehensive review [PDF]
International Journal of Data Science and Analysis, 2021 Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix.
Sajad Fathi Hafshejani, Z. Moaberfard
semanticscholar +1 more source
Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization
IEEE Access, 2018 Recommender Systems present a high-level of sparsity in their ratings matrices. The collaborative filtering sparse data makes it difficult to: 1) compare elements using memory-based solutions; 2) obtain precise models using model-based solutions; 3) get ...
Jesus Bobadilla +3 more
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Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion [PDF]
Jisuanji kexue, 2021 An effective algorithm for unmixing hyperspectral and panchromatic images of non-negative matrix factorization based on spectral reconstruction constraint is proposed.Firstly,this algorithm employs the regularization with minimum spectral reconstruction ...
GUAN Zheng, DENG Yang-lin, NIE Ren-can
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Robust capped norm dual hyper-graph regularized non-negative matrix tri-factorization
Mathematical Biosciences and Engineering, 2023 Non-negative matrix factorization (NMF) has been widely used in machine learning and data mining fields. As an extension of NMF, non-negative matrix tri-factorization (NMTF) provides more degrees of freedom than NMF.
Jiyang Yu +3 more
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IET Image Processing, 2022 Benefiting from the good physical interpretations and low computational complexity, non‐negative matrix factorization (NMF) has attracted wide attentions in data representation learning tasks.
Yanfeng Sun +4 more
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