Results 101 to 110 of about 82,948 (222)
Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering
IEEE Access, 2019 As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity.
Wei Yan +3 more
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, 2010 We propose a two-step algorithm for the construction of a Hidden Markov Model (HMM) of assigned size, i.e. cardinality of the state space of the underlying Markov chain, whose $n$-dimensional distribution is closest in divergence to a given distribution.
Finesso, L., Grassi, A., Spreij, P.
core
Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization
Tongxin xuebao, 2020 To address problems that the effectiveness of feature learned from real noisy data by classical nonnegative matrix factorization method,a novel sparsity induced manifold regularized convex nonnegative matrix factorization algorithm (SGCNMF) was proposed ...
Feiyue QIU +3 more
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Parallel Nonnegative Matrix Factorization with Manifold Regularization
Journal of Electrical and Computer Engineering, 2018 Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices.
Fudong Liu, Zheng Shan, Yihang Chen
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A Label-Embedding Online Nonnegative Matrix Factorization Algorithm
IEEE Access, 2019 Nonnegative matrix factorization is a widely used data processing method, which has been applied in many fields, such as data dimension reduction and feature extraction.
Zhibo Guo, Ying Zhang
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Approximate Nonnegative Matrix Factorization via Alternating Minimization
, 2004 In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix $V \in \R_+^{m\times n}$ find, for assigned $k$, nonnegative matrices $W\in\R_+^{m\times k}$ and $H\in\R_+^{k\times n}$ such that $V ...
Finesso, Lorenzo, Spreij, Peter
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Complexity, 2019 Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of ...
Yong-Jing Hao +4 more
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Nonnegative factorization and the maximum edge biclique problem [PDF]
Nonnegative matrix factorization (NMF) is a data analysis technique based on the approximation of a nonnegative matrix with a product of two nonnegative factors, which allows compression and interpretation of nonnegative data. In this paper, we study the
GILLIS, Nicolas, GLINEUR, François
core
UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data. [PDF]
Brief Bioinform, 2023 Abe K, Shimamura T.
europepmc +1 more source
Global and Local Similarity Learning in Multi-Kernel Space for Nonnegative Matrix Factorization. [PDF]
Knowl Based Syst, 2023 Peng C +5 more
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