Results 11 to 20 of about 53,359 (288)
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
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Improved SOCFS Algorithm Based on Triplet Ordinal Locality [PDF]
Jisuanji gongcheng, 2020 Features selection is commonly used in dimensionality reduction of machine learning,but existing unsupervised feature selection algorithms often ignore the influence of ordinal locality on feature selection while preserving the local structure of ...
WU Changming, ZHAO Xingtao, LIU Kexin
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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 and effectiveness of machine learning models with high-dimensional data. In this paper, we propose a
Kai Han 0002 +4 more
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Robust Low-rank Self-representation Feature Selection Algorithm [PDF]
Jisuanji gongcheng, 2017 Since unsupervised feature selection algorithms do not have label information and also ignore the low-rank characteristics of the data,this paper proposes a new low-rank feature selection algorithm based on self-representation method.In the loss function,
HU Rongyao,LIU Xingyi,CHENG Debo,HE Wei,LUO Yan
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Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
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Review of feature selection approaches based on grouping of features [PDF]
PeerJ, 2023 With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an
Cihan Kuzudisli +4 more
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Feature weighting as a tool for unsupervised feature selection [PDF]
Information Processing Letters, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Deepak Panday +2 more
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Fairness-Aware Unsupervised Feature Selection [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 Feature selection is a prevalent data preprocessing paradigm for various learning tasks. Due to the expensive cost of acquiring supervision information, unsupervised feature selection sparks great interests recently. However, existing unsupervised feature selection algorithms do not have fairness considerations and suffer from a high risk of amplifying
Xiaoying Xing +3 more
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Unsupervised Dual Learning for Feature and Instance Selection
IEEE Access, 2020 Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative ...
Liang Du, Xin Ren, Peng Zhou, Zhiguo Hu
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Journal of Information Science Theory and Practice, 2021 Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with ...
Qusay Bsoul +3 more
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