Results 1 to 10 of about 192,119 (178)
Utility metric for unsupervised feature selection [PDF]
PeerJ Computer Science, 2021 Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features.
Amalia Villa +4 more
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AutoEncoder Inspired Unsupervised Feature Selection [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance ...
Han, Kai +4 more
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Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection [PDF]
Entropy, 2023 In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect ...
Ni Li, Manman Peng, Qiang Wu
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Unsupervised feature selection algorithm based on L 2,p-norm feature reconstruction. [PDF]
PLoS ONETraditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces.
Wei Liu +5 more
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Using the Kriging Correlation for unsupervised feature selection problems [PDF]
Scientific Reports, 2022 This paper proposes a KC Score to measure feature importance in clustering analysis of high-dimensional data. The KC Score evaluates the contribution of features based on the correlation between the original features and the reconstructed features in the
Cheng-Han Chua +2 more
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Group Based Unsupervised Feature Selection [PDF]
Advances in Knowledge Discovery and Data Mining24th Pacific-Asia Conference, 2020 Unsupervised feature selection is an important task in machine learning applications, yet challenging due to the unavailability of class labels. Although a few unsupervised methods take advantage of external sources of correlations within feature groups in feature selection, they are limited to genomic data, and suffer poor accuracy because they ignore
Perera K, Chan J, Karunasekera S.
europepmc +3 more sources
Deep unsupervised feature selection by discarding nuisance and correlated features. [PDF]
Neural Netw, 2022 Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance features can be identified using the Laplacian score criterion, which evaluates the importance of a given feature via its consistency with the Graph Laplacians' leading ...
Shaham U +3 more
europepmc +5 more sources
JMIR Medical InformaticsBackgroundThe application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems.
Peyman Ghasemi, Joon Lee
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Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking [PDF]
Jisuanji kexue, 2023 High dimensional data is often encountered in many data analysis tasks.Feature selection techniques aim to find the most representative features from the original high-dimensional data.Due to the lack of class label information,it is much more difficult ...
LIANG Yunhui, GAN Jianwen, CHEN Yan, ZHOU Peng, DU Liang
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Unsupervised Feature Selection Based on Self-configuration Approaches using Multidimensional Scaling
Jambura Journal of Mathematics, 2023 Some researchers often collect features so the principal information does not lose. However, many features sometimes cause problems. The truth of analysis results will decrease because of the irrelevant or repetitive features.
Ridho Ananda +4 more
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