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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.
P. Mitra, C.A. Murthy, S.K. Pal
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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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Unsupervised Feature Selection with Adaptive Structure Learning [PDF]
, 2015 The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using
Alelyani S. +12 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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Feature subset selection and ranking for data dimensionality reduction [PDF]
, 2007 A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature ...
Billings, S.A., Wei, H.L.
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Unsupervised morphological segmentation for images [PDF]
, 1993 This paper deals with a morphological approach to unsupervised image segmentation. The proposed technique relies on a multiscale Top-Down approach allowing a hierarchical processing of the data ranging from the most global scale to the most detailed one.
Salembier Clairon, Philippe Jean
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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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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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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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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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