Results 51 to 60 of about 7,174 (250)
A projective approach to nonnegative matrix factorization
, 2021 In data science and machine learning, the method of nonnegative matrix factorization (NMF) is a powerful tool that enjoys great popularity. Depending on the concrete application, there exist several subclasses each of which performs a NMF under certain ...
Groetzner, Patrick
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Business Strategy and the Environment, EarlyView.ABSTRACT Firms' strategic decision‐making relies not only on their own information but also on that disclosed by supply chain partners. While research recognizes buyers' importance in green innovation, the impact of their information disclosure remains underdeveloped. Drawing on the extended resource‐based view, this study investigates the joint effect
Yang Yang, Yan Jiang
wiley +1 more source
Nonnegative matrix factorization requires irrationality [PDF]
, 2017 Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. A longstanding open question, posed by Cohen and Rothblum in 1993, is
Kiefer, Stefan +14 more
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Chronic Diseases and Translational Medicine, EarlyView.Features the major cell type compositions among colon and liver metastasis. (A) The study design for single‐cell data analysis. (B) The annotated major cell types. Each type was labeled with a special color. (C) Dot plot of canonical marker genes for major cell types. (D) Bar plot of the percentage for each cell type in individuals. (E) Bar plot of the
Zhixun Zhao +10 more
wiley +1 more source
Mathematics, 2023 NONNEGATIVE matrix factorization (NMF) is an effective technique for dimensionality reduction of high-dimensional data for tasks such as machine learning and data visualization.
Jie Li, Yaotang Li, Chaoqian Li
doaj +1 more source
Spectro-temporal post-enhancement using MMSE estimation in NMF based single-channel source separation [PDF]
, 2013 We propose to use minimum mean squared error (MMSE) estimates to enhance the signals that are separated by nonnegative matrix factorization (NMF). In single channel source separation (SCSS), NMF is used to train a set of basis vectors for each source ...
Erdoğan, Hakan +3 more
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Stochastic Gradient Descent in High Dimensions for Multi‐Spiked Tensor PCA
Communications on Pure and Applied Mathematics, EarlyView.ABSTRACT We study the high‐dimensional dynamics of online stochastic gradient descent (SGD) for the multi‐spiked tensor model. This multi‐index model arises from the tensor principal component analysis (PCA) problem with multiple spikes, where the goal is to estimate the unknown signal vectors within the N$N$‐dimensional unit sphere through maximum ...
Gérard Ben Arous +2 more
wiley +1 more source
A new Approach for Building Recommender System Using Non-Negative Matrix Factorization Method
پژوهشهای ریاضی, 2021 Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is decomposed into components that are more interrelated and divide the data into sections where ...
nushin shahrokhi, somayeh arabi narie
doaj
Projective robust nonnegative factorization
, 2016 Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature ...
You, Jane +11 more
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Intraday Functional PCA Forecasting of Cryptocurrency Returns
Journal of Forecasting, EarlyView.ABSTRACT We study the functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account.
Joann Jasiak, Cheng Zhong
wiley +1 more source