Results 111 to 120 of about 1,627,028 (236)

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, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 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, Bowen CHEN, Tieming CHEN, Guodao ZHANG   +3 more
doaj   +2 more sources

A Constrained Sparse Algorithm for Nonnegative Matrix Factorization

工程科学与技术, 2015
:Aiming at the lack of sparseness of factorization matrix in the nonnegative matrix factorization (NMF) algorithm,a new constrained NMF algorithm was proposed.A sparseness constraint was added to the original nonnegative matrix factorization (NMF ...
李臣明, 张师明, 李昌利
doaj  

Graph‐Laplacian modeling of spatiotemporal effects for house price estimation

Real Estate Economics, EarlyView.
Abstract Many variables involve the modeling of spatial effects, and their dynamics over time. This article presents a linear model in which spatiotemporal random effects are modeled by graph‐Laplacians. A graph‐Laplacian flexibly encodes adjacency in both space and time, in our case not depending on unknown parameters. The graph‐Laplacian can be input
Willem P Sijp, Marc K. Francke
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

Multidimensional unipolar IRT and applications to the measurement of print exposure

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Item response theory (IRT) has been a prominent modelling framework in educational and psychological measurement. Traditional IRT models are bipolar, commonly assuming symmetric measurement link functions and symmetric trait distributions over a latent continuum unbounded at both ends. However, many measured constructs, such as print exposure,
Qi (Helen) Huang, Daniel M. Bolt
wiley   +1 more source

Improved Graph-Regularized Discriminative Nonnegative Matrix Factorization for Semi-Supervised Clustering

IEEE Access
Nonnegative matrix factorization (NMF) is an effective dimensionality reduction and representation learning technique that captures the intrinsic structure of nonnegative data by learning low-dimensional, parts-based representations.
Xuzhu Shen, Jie Li
doaj   +1 more source

A Label-Embedding Online Nonnegative Matrix Factorization Algorithm

IEEE Access, 2019
Nonnegative matrix factorization is a widely used data processing method, which has been applied in many fields, such as data dimension reduction and feature extraction.
Zhibo Guo, Ying Zhang
doaj   +1 more source

Environmental Impact Shaping a Firm's Zero Leverage Decision: Analysing Debt Demand and Supply Determinants

European Financial Management, EarlyView.
ABSTRACT Zero‐leverage firms remain a puzzle in corporate finance. We propose a supply‐side mechanism linking environmental impact to debt access. Because creditors favour firms with high negative externalities and strong cash flows, environmentally friendly firms with high initial costs face tighter credit constraints.
Paolo Saona, Antonio Renzi, Gianluca Vagnani, Pietro Taragoni   +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