Results 101 to 110 of about 141,420 (324)
Simplicial nonnegative matrix factorization
The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2013 Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mining, especially for dimension reduction and component analysis. It is employed widely in different fields such as information retrieval, image processing, etc. After a decade of fast development, severe limitations still remained in NMFs methods including high ...
null Duy Khuong Nguyen +2 more
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A multilevel approach for nonnegative matrix factorization [PDF]
Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing ...
GILLIS, Nicolas, GLINEUR, François
core
Loss Behavior in Supervised Learning With Entangled States
Advanced Quantum Technologies, EarlyView.Entanglement in training samples supports quantum supervised learning algorithm in obtaining solutions of low generalization error. Using analytical as well as numerical methods, this work shows that the positive effect of entanglement on model after training has negative consequences for the trainability of the model itself, while showing the ...
Alexander Mandl +4 more
wiley +1 more source
Alternating Block Linearized Bregman Iterations for Regularized Nonnegative Matrix Factorization [PDF]
, 2023 C. Beier, Hui Zhang
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Data‐Based Refinement of Parametric Uncertainty Descriptions
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT We consider dynamical systems with a linear fractional representation involving parametric uncertainties which are either constant or varying with time. Given a finite‐horizon input‐state or input‐output trajectory of such a system, we propose a numerical scheme which iteratively improves the available knowledge about the involved constant ...
Tobias Holicki, Carsten W. Scherer
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
doaj +1 more source
Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2019 Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields.
Lin Zhang +3 more
doaj +1 more source
Random Projection Streams for (Weighted) Nonnegative Matrix Factorization
, 2021 Farouk Yahaya +3 more
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Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation [PDF]
, 2020 Guang‐Jing Song +2 more
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On Restricted Nonnegative Matrix Factorization
, 2016 Full version of an ICALP'16 ...
Chistikov, D +4 more
openaire +5 more sources