Results 31 to 40 of about 14,760 (255)
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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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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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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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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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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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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Bayesian Non-negative Matrix Factorization [PDF]
, 2009 We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates.
Mikkel N. Schmidt +2 more
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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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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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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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