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
doaj +1 more source
Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
, 2018 Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components ...
Hook, James +2 more
core +1 more source
On Restricted Nonnegative Matrix Factorization
, 2016 Full version of an ICALP'16 ...
Chistikov, D +4 more
openaire +5 more sources
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
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
doaj +2 more sources
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
Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank–Wolfe Approach [PDF]
, 2022 Tri Nguyen, Xiao Fu, Ruiyuan Wu
openalex +1 more source
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
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
doaj +1 more source
Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization
Advances in Mathematical Physics, 2020 A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed for traditional process monitoring methods which often adopt global model of data and ignore local information of data. Firstly, the
Yan Wang +5 more
doaj +1 more source