Results 1 to 10 of about 53,359 (288)
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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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 +4 more sources
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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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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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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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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Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study [PDF]
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 using feature similarity [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002 In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast.
C A Murthy, Sankar K Pal
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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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Algorithms, 2023 Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature ...
Deepesh Chugh +5 more
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