Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection.
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| Title: | Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection. |
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| Authors: | Yuan, Haoliang1 hunteryuan@126.com, Li, Junyu1, Lai, Loi Lei1 l.l.lai@gdut.edu.cn, Tang, Yuan Yan2,3 |
| Source: | Pattern Recognition. May2019, Vol. 89, p119-133. 15p. |
| Subjects: | Sparse matrix software, Spectrum analysis, Matrix analytic methods, Labels, Classification |
| Abstract: | Highlights • We propose a novel two-dimensional unsupervised feature selection model to directly conduct the feature selection on the matrix data. For sparse matrix regression under unsupervised case, the key point is to connect the matrix and its cluster label. In this work, we utilize a nonnegative spectral clustering to compute a cluster label matrix. To integrate the merits of both sparse matrix regression and nonnegative spectral clustering, we joint these two techniques as a whole optimization model for feature selection. • Our JSMRNS extends the SMR model to its unsupervised case. Although JSMRNS is the combination of sparse matrix regression and nonnegative spectral clustering, our JSMRNS can be viewed as the two-dimensional extension of the NDFS model through theoretical analysis, which ensures that our JSMRNS works. Moreover, in certain situation, JSMRNS can approximate the vector-based regression model, i.e., NDFS. • We devise an efficient optimization strategy to solve this joint sparse matrix regression and nonnegative spectral analysis optimization problem. Some theoretical discussions are presented to show the convergence behavior and computational complexity of the optimization strategy. Moreover, we give the detailed discussions to compare with the subspace learning and some relevant models. Extensive experimental results also confirm the effectiveness of our proposed method. Abstract Unsupervised feature selection is a challenging task to gain relevant features for improving learning performance due to lack of the label information. Traditional unsupervised feature selection methods are often vector-based, which may ignore the location information of original matrix element. In this paper, we propose a joint sparse matrix regression and nonnegative spectral analysis model for two-dimensional unsupervised feature selection. To obtain proper label information under unsupervised condition, we adopt a nonnegative spectral clustering technique to yield the clustering labels as the pseudo class labels. To directly select the relevant feature on matrix data, we construct a regression relationship between matrix data and the pseudo class labels by deploying left and right regression matrices. Our proposed method can integrate the merits of both sparse matrix regression and nonnegative spectral clustering for feature selection. An efficient optimization algorithm is designed to solve our proposed optimization problem. Extensive experimental results on clustering and classification demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 135055531 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Haoliang%22">Yuan, Haoliang</searchLink><relatesTo>1</relatesTo><i> hunteryuan@126.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Junyu%22">Li, Junyu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lai%2C+Loi+Lei%22">Lai, Loi Lei</searchLink><relatesTo>1</relatesTo><i> l.l.lai@gdut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Yuan+Yan%22">Tang, Yuan Yan</searchLink><relatesTo>2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. May2019, Vol. 89, p119-133. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sparse+matrix+software%22">Sparse matrix software</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+analytic+methods%22">Matrix analytic methods</searchLink><br /><searchLink fieldCode="DE" term="%22Labels%22">Labels</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights • We propose a novel two-dimensional unsupervised feature selection model to directly conduct the feature selection on the matrix data. For sparse matrix regression under unsupervised case, the key point is to connect the matrix and its cluster label. In this work, we utilize a nonnegative spectral clustering to compute a cluster label matrix. To integrate the merits of both sparse matrix regression and nonnegative spectral clustering, we joint these two techniques as a whole optimization model for feature selection. • Our JSMRNS extends the SMR model to its unsupervised case. Although JSMRNS is the combination of sparse matrix regression and nonnegative spectral clustering, our JSMRNS can be viewed as the two-dimensional extension of the NDFS model through theoretical analysis, which ensures that our JSMRNS works. Moreover, in certain situation, JSMRNS can approximate the vector-based regression model, i.e., NDFS. • We devise an efficient optimization strategy to solve this joint sparse matrix regression and nonnegative spectral analysis optimization problem. Some theoretical discussions are presented to show the convergence behavior and computational complexity of the optimization strategy. Moreover, we give the detailed discussions to compare with the subspace learning and some relevant models. Extensive experimental results also confirm the effectiveness of our proposed method. Abstract Unsupervised feature selection is a challenging task to gain relevant features for improving learning performance due to lack of the label information. Traditional unsupervised feature selection methods are often vector-based, which may ignore the location information of original matrix element. In this paper, we propose a joint sparse matrix regression and nonnegative spectral analysis model for two-dimensional unsupervised feature selection. To obtain proper label information under unsupervised condition, we adopt a nonnegative spectral clustering technique to yield the clustering labels as the pseudo class labels. To directly select the relevant feature on matrix data, we construct a regression relationship between matrix data and the pseudo class labels by deploying left and right regression matrices. Our proposed method can integrate the merits of both sparse matrix regression and nonnegative spectral clustering for feature selection. An efficient optimization algorithm is designed to solve our proposed optimization problem. Extensive experimental results on clustering and classification demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.patcog.2019.01.014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 119 Subjects: – SubjectFull: Sparse matrix software Type: general – SubjectFull: Spectrum analysis Type: general – SubjectFull: Matrix analytic methods Type: general – SubjectFull: Labels Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuan, Haoliang – PersonEntity: Name: NameFull: Li, Junyu – PersonEntity: Name: NameFull: Lai, Loi Lei – PersonEntity: Name: NameFull: Tang, Yuan Yan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 89 Titles: – TitleFull: Pattern Recognition Type: main |
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