Results 51 to 60 of about 53,359 (288)
Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
Algorithms, 2020 Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems ...
Ibraheem Al-Jadir +3 more
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IET Image Processing, 2023 Unsupervised feature selection is a dimensionality reduction method and has been widely used as an important and indispensable preprocessing step in many tasks.
Lingli Guo, Xiuhong Chen
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Central nervous system (CNS) inflammatory demyelinating syndromes, including multiple sclerosis (MS), aquaporin‐4 antibody–positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody–associated disease (MOGAD), occasionally overlap.
Bade Gulec +6 more
wiley +1 more source
IEEE Access, 2019 The typical inaccuracy of data gathering and preparation procedures makes erroneous and unnecessary information to be a common issue in real-world applications.
Jose A. Saez, Emilio Corchado
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Towards an Unsupervised Feature Selection Method for Effective Dynamic Features
IEEE Access, 2021 Dynamic features applications present new obstacles for the selection of streaming features. The dynamic features applications have various characteristics: a) features are processed sequentially while the number of instances is fixed; and b) the feature
Naif Almusallam +5 more
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Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection
IEEE Access, 2020 In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of ...
Hua Zhao +3 more
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Unsupervised Feature Selection with Local Structure Learning
, 2018 Conventional graph-based unsupervised feature selection approaches carry out the feature selection requiring two stages: first, constructing the data similarity matrix and next performing feature selection.
Feiping Nie +5 more
core +1 more source
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
RMFRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
Algorithms, 2022 In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the ...
Shumin Lai +5 more
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