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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 Mining, 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 +5 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
doaj +2 more sources
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
doaj +2 more sources
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
exaly +3 more sources
Unsupervised Feature Selection for Noisy Data [PDF]
, 2019 Feature selection techniques are enormously applied in a variety of data analysis tasks in order to reduce the dimensionality. According to the type of learning, feature selection algorithms are categorized to: supervised or unsupervised. In unsupervised learning scenarios, selecting features is a much harder problem, due to the lack of class labels ...
Mahdavi, Kaveh +2 more
openaire +3 more sources
A niching memetic algorithm for simultaneous clustering and feature selection [PDF]
, 2008 Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm.
Liu, X, Sheng, W, Fairhurst, M
core +6 more sources
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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