Results 21 to 30 of about 1,535 (215)
Kernel Joint Non-Negative Matrix Factorization for Genomic Data
IEEE Access, 2021 The multi-modal or multi-view integration of data has generated a wide range of applicability in pattern extraction, clustering, and data interpretation.
Diego Salazar +4 more
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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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Multifrontal Non-negative Matrix Factorization
, 2020 Non-negative matrix factorization (Nmf) is an important tool in high-performance large scale data analytics with applications ranging from community detection, recommender system, feature detection and linear and non-linear unmixing. While traditional Nmf works well when the data set is relatively dense, however, it may not extract sufficient structure
Piyush Sao, Ramakrishnan Kannan
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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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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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Optimization and expansion of non-negative matrix factorization
BMC Bioinformatics, 2020 Background Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics.
Xihui Lin, Paul C. Boutros
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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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Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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Non-negative Matrix Factorization Parallel Optimization Algorithm Based on Lp-norm [PDF]
Jisuanji kexueNon-negative matrix factorization algorithm is an important tool for image clustering,data compression and feature extraction.Traditional non-negative matrix factorization algorithms mostly use Euclidean distance to measure reconstruction error,which has
HUANG Lulu, TANG Shuyu, ZHANG Wei, DAI Xiangguang
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Majorization-Minimization Algorithm for Discriminative Non-Negative Matrix Factorization
IEEE Access, 2020 This paper proposes a basis training algorithm for discriminative non-negative matrix factorization (NMF) with applications to single-channel audio source separation.
Li Li, Hirokazu Kameoka, Shoji Makino
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