Results 101 to 110 of about 7,174 (250)
Complexity, 2019 Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of ...
Yong-Jing Hao +4 more
doaj +1 more source
Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction
, 2016 As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining.
Li BF(李冰锋) +2 more
core
International Statistical Review, EarlyView.Summary Understanding the spread of infectious diseases such as COVID‐19 is crucial for informed decision‐making and resource allocation. A critical component of disease behaviour is the velocity with which disease spreads, defined as the rate of change between time and space.
Fernando Rodriguez Avellaneda +2 more
wiley +1 more source
ACCOUNTING FOR PHASE CANCELLATIONS IN NON-NEGATIVE MATRIX FACTORIZATION USING WEIGHTED DISTANCES
, 2014 (c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Ewert, S, Plumbley, MD, Sandler, M, IEEE
core
On reduced rank nonnegative matrix factorization for symmetric nonnegative matrices
, 2004 Let V∈Rm,n be a nonnegative matrix. The nonnegative matrix factorization (NNMF) problem consists of finding nonnegative matrix factors W∈Rm,r and H∈Rr,n such that V≈WH.
Neumann, Michael +3 more
core +1 more source
Nonnegative matrix factorization of phonocardiograms for heart rate detection [PDF]
, 2022 openPhonocardiograms (PCGs) are recordings of the sounds and murmurs made by the heart detected through specialized microphones placed on a patient's thorax.
CHINELLATO, ERIK
core
A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces
International Statistical Review, EarlyView.Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri +3 more
wiley +1 more source
Using underapproximations for sparse nonnegative matrix factorization
, 2008 Nonnegative matrix factorization consists in (approximately) factorizing a nonnegative data matrix by the product of two low-rank nonnegative matrices. It has been successfully applied as a data analysis technique in numerous domains, e.g., text mining ...
François Glineur +3 more
core +1 more source
Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data
, 2020 Continuous advancement of brain imaging techniques has witnessed data analysis methods evolving from matrix component analysis to tensor component analysis, from individual analysis to group analysis regarding the analysis of brain data with multi-set/
Wang, Xiulin
core +1 more source
International Transactions in Operational Research, EarlyView.Abstract In this paper, we address the problem of routing a fleet of electric vehicles (EVs) to serve a set of customers, geographically distributed, within their time windows. We assume that EVs may also be recharged en route, and the amount of energy recharged at a recharging station (RS) is a decision variable itself, that is, partial recharges are ...
Maurizio Bruglieri +3 more
wiley +1 more source