Results 41 to 50 of about 23,872 (265)
Sparse Deep Nonnegative Matrix Factorization
Big Data Mining and Analytics, 2020 Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. However, deep learning networks, with their carefully designed hierarchical structure,
Zhenxing Guo, Shihua Zhang
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Quaternion Matrix Factorization for Low-Rank Quaternion Matrix Completion
Mathematics, 2023 The main aim of this paper is to study quaternion matrix factorization for low-rank quaternion matrix completion and its applications in color image processing.
Jiang-Feng Chen +3 more
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Stochastic Matrix Factorization [PDF]
SSRN Electronic Journal, 2016 24 pages, 4 figures, 5 ...
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Complexity, 2018 We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the ...
Xiangguang Dai +2 more
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Discriminant projective non-negative matrix factorization. [PDF]
PLoS ONE, 2013 Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan +4 more
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Matrix Factorization as Search [PDF]
, 2012 Simplex Volume Maximization (SiVM) exploits distance geometry for efficiently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques.
Kristian Kersting +3 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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Network Embedding Using Deep Robust Nonnegative Matrix Factorization
IEEE Access, 2020 As an effective technique to learn low-dimensional node features in complicated network environment, network embedding has become a promising research direction in the field of network analysis.
Chaobo He +5 more
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Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
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Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization
IEEE Access, 2019 The rapid development of the Internet of Things (IoT) and e-commerce has brought a lot of convenience to people's lives. IoT applications generate a large number of services and user data.
Weimin Li +6 more
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