Results 81 to 90 of about 32,711 (308)
Stretched non-negative matrix factorization
npj Computational MaterialsA novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis.
Ran Gu +11 more
doaj +1 more source
Alternating Block Linearized Bregman Iterations for Regularized Nonnegative Matrix Factorization [PDF]
, 2023 C. Beier, Hui Zhang
openalex +1 more source
Neighborhood Preserving Convex Nonnegative Matrix Factorization [PDF]
Mathematical Problems in Engineering, 2014 The convex nonnegative matrix factorization (CNMF) is a variation of nonnegative matrix factorization (NMF) in which each cluster is expressed by a linear combination of the data points and each data point is represented by a linear combination of the cluster centers.
Wei, Jiang, Min, Li, Zhang, Yongqing
openaire +2 more sources
Joint Estimation and Bandwidth Selection in Partially Parametric Models
Journal of Applied Econometrics, EarlyView.ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown ...
Daniel J. Henderson +2 more
wiley +1 more source
Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
doaj +1 more source
Robust semi-supervised nonnegative matrix factorization [PDF]
2015 International Joint Conference on Neural Networks (IJCNN), 2015 Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a wide range of applications such as data clustering, pattern recognition and computer vision. Real-world data are often sparse and noisy which may reduce the accuracy of representations. And a small part of data may
Wang, Jing +3 more
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Journal of Applied Econometrics, EarlyView.ABSTRACT The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units.
David Powell
wiley +1 more source
Link prediction based on non-negative matrix factorization. [PDF]
PLoS ONE, 2017 With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like ...
Bolun Chen +4 more
doaj +1 more source
Context aware nonnegative matrix factorization clustering [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 6 pages, 3 figures.
TRIPODI, ROCCO +2 more
openaire +3 more sources
Predicting Infrared Optical Properties of Materials Using Machine Learning Interatomic Potentials
Materials Genome Engineering Advances, EarlyView.This work proposes a new fast computing framework for infrared reflectance spectra, MTP‐FIRE, based on machine learning potential, which can achieve the same accuracy as the existing first‐principles calculation, but can be two orders of magnitude faster on average.
Lianduan Zeng +8 more
wiley +1 more source