Nonnegative Matrix Factorization: A Comprehensive Review

By Yu-Xiong Wang, Yujin Zhang. IEEE Transactions on Knowledge and Data Engineering, 2013

937 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Urban Studies

View Paper: https://www.semanticscholar.org/paper/10f984a209124fb8eebd0c53b83fa265f18776b7

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In Computer Vision and Pattern Recognition

In Urban Studies

Citations in the corpus

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “Furthermore, Alsheikh et al. [118] recommended deep learning methods [21] and the non-negative matrix factorization algorithm [162] for more efﬁcient unsupervised learning methods for the WSNs.”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “To overcome this limitation in this centralized system, we recommend investigating unsupervised machine learning algorithms for automatic feature extraction such as the deep learning methods [9] and the non-negative matrix factorization algorithm [110].”
Comment-based multi-view clustering of web 2.0 items (2014, neutral): “NMF has shown good performance and much work has been done in both applying NMF to different problem areas as well as on studying NMF itself [39].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “However, there exist many classes of algorithms [100] for NMF having different generalization properties, for example, two of them are analyzed in [101], one of which minimizes the least square error and while the other focuses on the Kullback-Leibler divergence keeping algorithm convergence intact.”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, methodology): “Applications of NMF to biomedical data include discriminative feature selection from time–frequency representation of EEG data [45], feature extraction from brain CT images [46], and microarray gene data reduction for visualization and clustering purposes [47].”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, methodology): “Gao et al. [88] have proposed a novel SAR image change detection method based on deep semi-nonnegative matrix factorization (Deep SemiNMF) [89] and singular value decomposition (SVD) networks [90].”
Model recommendation: Generating object detectors from few samples (2015, methodology): “Specifically, we consider approaches based on singular value decomposition (SVD) and non-negative matrix factorization (NMF), which we describe briefly in this section.”
Fast Robust PCA on Graphs (2015, neutral): “Fast Robust PCA on Graphs Nauman Shahid ∗ , Nathanael Perraudin, Vassilis Kalofolias, Gilles Puy † , Pierre Vandergheynst Email: { nauman.shahid, Signal Processing nathanael.perraudin, vassilis.kalofolias, pierre.vandergheynst @epﬂ.ch, † gilles.puy@inria.fr Switzerland Laboratory 2”