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Scalable and Flexible Unsupervised Feature Selection.

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Title: Scalable and Flexible Unsupervised Feature Selection.
Authors: Hu, Haojie1 haojiehu705@gmail.com, Wang, Rong2 wangrong07@tsinghua.org.cn, Yang, Xiaojun3 yangxj18@gdut.edu.cn, Nie, Feiping2 feipingnie@gmail.com
Source: Neural Computation. Mar2019, Vol. 31 Issue 3, p517-537. 21p. 4 Charts, 4 Graphs.
Subjects: Feature selection, Mathematical models of learning, Algorithms, Mathematical statistics, Graph theory, Mathematical optimization
Abstract: Recently, graph-based unsupervised feature selection algorithms (GUFS) have been shown to efficiently handle prevalent high-dimensional unlabeled data. One common drawback associated with existing graph-based approaches is that they tend to be time-consuming and in need of large storage, especially when faced with the increasing size of data. Research has started using anchors to accelerate graph-based learning model for feature selection, while the hard linear constraint between the data matrix and the lower-dimensional representation is usually overstrict in many applications. In this letter, we propose a flexible linearization model with anchor graph and ℓ 21 -norm regularization, which can deal with large-scale data sets and improve the performance of the existing anchor-based method. In addition, the anchor-based graph Laplacian is constructed to characterize the manifold embedding structure by means of a parameter-free adaptive neighbor assignment strategy. An efficient iterative algorithm is developed to address the optimization problem, and we also prove the convergence of the algorithm. Experiments on several public data sets demonstrate the effectiveness and efficiency of the method we propose. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Mar2019, Vol. 31 Issue 3, p517-537. 21p. 4 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+of+learning%22">Mathematical models of learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: Recently, graph-based unsupervised feature selection algorithms (GUFS) have been shown to efficiently handle prevalent high-dimensional unlabeled data. One common drawback associated with existing graph-based approaches is that they tend to be time-consuming and in need of large storage, especially when faced with the increasing size of data. Research has started using anchors to accelerate graph-based learning model for feature selection, while the hard linear constraint between the data matrix and the lower-dimensional representation is usually overstrict in many applications. In this letter, we propose a flexible linearization model with anchor graph and ℓ 21 -norm regularization, which can deal with large-scale data sets and improve the performance of the existing anchor-based method. In addition, the anchor-based graph Laplacian is constructed to characterize the manifold embedding structure by means of a parameter-free adaptive neighbor assignment strategy. An efficient iterative algorithm is developed to address the optimization problem, and we also prove the convergence of the algorithm. Experiments on several public data sets demonstrate the effectiveness and efficiency of the method we propose. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1162/neco_a_01163
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      – Code: eng
        Text: English
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        PageCount: 21
        StartPage: 517
    Subjects:
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Mathematical models of learning
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Mathematical statistics
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      – SubjectFull: Graph theory
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      – SubjectFull: Mathematical optimization
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      – TitleFull: Scalable and Flexible Unsupervised Feature Selection.
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            NameFull: Wang, Rong
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            NameFull: Yang, Xiaojun
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              M: 03
              Text: Mar2019
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              Y: 2019
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            – TitleFull: Neural Computation
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