Results 91 to 100 of about 1,698,574 (297)
Discriminative Multiview Nonnegative Matrix Factorization for Classification
IEEE Access, 2019 Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination.
Weihua Ou +4 more
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Tight Semi-nonnegative Matrix Factorization [PDF]
Pattern Recognition and Image Analysis, 2020 The nonnegative matrix factorization is a widely used, flexible matrix decomposition, finding applications in biology, image and signal processing and information retrieval, among other areas. Here we present a related matrix factorization. A multi-objective optimization problem finds conical combinations of templates that approximate a given data ...
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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
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This study examines whether the relationship between environmental, social, and governance (ESG) performance and financial performance varies across legal systems in Europe. Drawing on institutional theory, we argue that legal origins shape how sustainability practices translate into financial outcomes.
Nuria Alcalde‐Fradejas +2 more
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On the geometric interpretation of the nonnegative rank [PDF]
The nonnegative rank of a nonnegative matrix is the minimum number of nonnegative rank-one factors needed to reconstruct it exactly. The problem of determining this rank and computing the corresponding nonnegative factors is difficult; however it has ...
GILLIS, Nicolas, GLINEUR, François
core
Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization
, 2018 © 2018 Society for Industrial and Applied Mathematics. In this paper, we study orthogonal nonnegative matrix factorization. We demonstrate the coefficient matrix can be sparse and low-rank in the orthogonal nonnegative matrix factorization.
Junjun Pan +3 more
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Deep Underground Science and Engineering, EarlyView.This graphical abstract presents a hierarchical adaptive strategy for selecting rheological models in intelligent backfill pipeline transportation. Temperature, fly ash content, and water‐cement ratio jointly influence slurry rheological nonlinearity, which is quantified using the Model Fidelity Gain (ΔR2).
Defeng Wang +3 more
wiley +1 more source
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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IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
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Diversified Bayesian Nonnegative Matrix Factorization
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data.
Maoying Qiao +4 more
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