Results 21 to 30 of about 53,359 (288)
Unsupervised Feature Selection with Latent Relationship Penalty Term
Axioms, 2023 With the exponential growth of high dimensional unlabeled data, unsupervised feature selection (UFS) has attracted considerable attention due to its excellent performance in machine learning.
Ziping Ma +3 more
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UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
Applied Sciences, 2023 In most machine learning (ML) applications, data that arrive from heterogeneous views (i.e., multiple heterogeneous sources of data) are more likely to provide complementary information than does a single view.
Fawaz Alarfaj +5 more
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Remote Sensing, 2018 In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available.
Patrick Erik Bradley +2 more
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Structure Preserving Non-negative Feature Self-Representation for Unsupervised Feature Selection
IEEE Access, 2017 Inspired by the importance of self-representation and structure-preserving ability of features, in this paper, we propose a novel unsupervised feature selection algorithm named structure-preserving non-negative feature self-representation (SPNFSR).
Wei Zhou +3 more
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ENTROPY BASED GREEDY UNSUPERVISED FEATURE SELECTION METHOD USING ROUGH SET THEORY FOR CLASSIFICATION
ICTACT Journal on Soft Computing, 2022 Feature selection technique attempts to select and remove irrelevant features while ensuring that an informative subset of features remains in the dataset.
Rubul Kumar Bania, Satyajit Sarmah
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Auto-UFSTool: An Automatic Unsupervised Feature Selection Toolbox for MATLAB [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Various data analysis research has recently become necessary in to find and select relevant features without class labels using Unsupervised Feature Selection (UFS) approaches. Despite the fact that several open-source toolboxes provide feature selection
Farhad Abedinzadeh Torghabeh +2 more
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Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification
Remote Sensing, 2022 A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes ...
Mateus Habermann +2 more
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Structure Preserving Unsupervised Feature Selection Based on Autoencoder and Manifold Regularization [PDF]
Jisuanji kexue, 2021 There are a lot of redundant and irrelevant features in high-dimensional data,which seriously affect the efficiency and quality of data mining and the generalization performance of machine learning.Therefore,feature selection has become an important ...
YANG Lei, JIANG Ai-lian, QIANG Yan
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Unsupervised Feature Selection on Data Streams [PDF]
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015 Massive data streams are continuously being generated from sources such as social media, broadcast news, etc., and typically these datapoints lie in high-dimensional spaces (such as the vocabulary space of a language). Timely and accurate feature subset selection in these massive data streams has important applications in model interpretation ...
Hao Huang 0007 +2 more
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IEEE Access, 2020 The open-circuit faults of power semiconductor devices in multilevel converters are generally diagnosed by analyzing circuit signals. For converters with five or more levels, the difficulty of fault detection increases with increasing topological ...
Shu Ye +4 more
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