Results 121 to 130 of about 32,711 (308)
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
A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization [PDF]
, 2020 Anthony Degleris, Nicolas Gillis
openalex +1 more source
Boosted unsupervised feature selection for tumor gene expression profiles
CAAI Transactions on Intelligence Technology, EarlyView.Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source
Optimising Wave Energy Plant Location Through Neutrosophic Multi‐Criteria Group Decision‐Making
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT The global shift towards sustainable energy has intensified research into renewable sources, particularly wave energy. Pakistan, with its long coastline, holds significant potential for wave energy development. However, identifying optimal locations for wave energy plants involves evaluating complex, multi‐faceted criteria.
Hafiz Muhammad Athar Farid +4 more
wiley +1 more source
, 2010 We propose a two-step algorithm for the construction of a Hidden Markov Model (HMM) of assigned size, i.e. cardinality of the state space of the underlying Markov chain, whose $n$-dimensional distribution is closest in divergence to a given distribution.
Finesso, L., Grassi, A., Spreij, P.
core
tidypopgen: Tidy population genetics in R
Methods in Ecology and Evolution, EarlyView.Abstract As genome‐wide data have become increasingly available, software libraries for their analysis have proliferated. While new tools for downstream analyses are constantly emerging, existing workflows are hindered by inefficiencies. Switching between coding languages and object types in the early stages of pipelines wastes researchers' time ...
Evelyn J. Carter +3 more
wiley +1 more source
Learning manifold to regularize nonnegative matrix factorization [PDF]
, 2014 Jim Jing-Yan Wang, Xin Gao
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Methods in Ecology and Evolution, EarlyView.Abstract The gut microbiota is increasingly recognized as a rich source of biological data that offers critical information on host health, including information on pathological conditions such as cancer, diabetes and other metabolic disorders. Inferring host health from gut bacterial composition using statistical analytical methods remains a challenge.
David Martin +3 more
wiley +1 more source
When in Doubt, Tax More Progressively? Uncertainty and Progressive Income Taxation
International Economic Review, EarlyView.ABSTRACT We study the optimal income tax problem under parameter uncertainty about household preferences and wage dynamics. We derive conditions characterizing how such uncertainty affects optimal tax policy. To quantify the effect, we estimate a life‐cycle model using US data and a Bayesian approach.
Minsu Chang, Chunzan Wu
wiley +1 more source
A flexible R package for nonnegative matrix factorization
BMC Bioinformatics, 2010 Background Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have
Seoighe Cathal, Gaujoux Renaud
doaj +1 more source