FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT
Linear local tangent space alignment（ LLTSA） is a dimensionality reduction method which is easily used to pattern recognition. However,it is an unsupervised dimensionality reduction method and only use global neighborhood parameter,when it used to high-dimensional data for dimensionality reduction,i...
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Editorial Office of Journal of Mechanical Strength
2018-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.007 |
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| author | XIE XiaoHua WANG QingHong |
| author_facet | XIE XiaoHua WANG QingHong |
| author_sort | XIE XiaoHua |
| collection | DOAJ |
| description | Linear local tangent space alignment（ LLTSA） is a dimensionality reduction method which is easily used to pattern recognition. However,it is an unsupervised dimensionality reduction method and only use global neighborhood parameter,when it used to high-dimensional data for dimensionality reduction,its incapacity of using part sample class label information and self-adaptive adjust neighborhood parameter while the samples space distribution changed. Aiming at the problems above,a semisupervised neighborhood self-adaptive linear local tangent space alignment（ SSNA-LLTSA） dimensionality reduction method is proposed in this paper. In SSNA-LLTSA, the distance between different points is adjusted by utilizing part class label information,thereby a new distance matrix is formed and the neighborhood is constructed through this new distance matrix. At the same time, the neighborhood parameters are self-adaptive adjusted according to probability density of each sample point neighborhood. The experiment results of classical 3D manifold,UCI datasets and bearing fault diagnosis show that the algorithm overcomes the drawbacks that the LLTSA has no supervision and the use of global unified neighborhood parameters and it is more effective to find the low dimensional nature of the data for improving the recognition accuracy and has certain superiority. |
| format | Article |
| id | doaj-art-508b1d68e8f04b51ae52226dcf0e31be |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-508b1d68e8f04b51ae52226dcf0e31be2025-01-15T02:31:27ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692018-01-01401056106230602986FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENTXIE XiaoHuaWANG QingHongLinear local tangent space alignment（ LLTSA） is a dimensionality reduction method which is easily used to pattern recognition. However,it is an unsupervised dimensionality reduction method and only use global neighborhood parameter,when it used to high-dimensional data for dimensionality reduction,its incapacity of using part sample class label information and self-adaptive adjust neighborhood parameter while the samples space distribution changed. Aiming at the problems above,a semisupervised neighborhood self-adaptive linear local tangent space alignment（ SSNA-LLTSA） dimensionality reduction method is proposed in this paper. In SSNA-LLTSA, the distance between different points is adjusted by utilizing part class label information,thereby a new distance matrix is formed and the neighborhood is constructed through this new distance matrix. At the same time, the neighborhood parameters are self-adaptive adjusted according to probability density of each sample point neighborhood. The experiment results of classical 3D manifold,UCI datasets and bearing fault diagnosis show that the algorithm overcomes the drawbacks that the LLTSA has no supervision and the use of global unified neighborhood parameters and it is more effective to find the low dimensional nature of the data for improving the recognition accuracy and has certain superiority.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.007Semi-supervisedNeighborhood self-adaptiveLinear local tangent space alignmentPattern recognition |
| spellingShingle | XIE XiaoHua WANG QingHong FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT Jixie qiangdu Semi-supervised Neighborhood self-adaptive Linear local tangent space alignment Pattern recognition |
| title | FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT |
| title_full | FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT |
| title_fullStr | FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT |
| title_full_unstemmed | FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT |
| title_short | FAULT RECOGNITION METHOD RESEARCH BASED ON SEMI-SUPERVISED NEIGHBORHOOD SELF-ADAPTIVE LINERA LOCAL TANGENT SPACE ALIGNMENT |
| title_sort | fault recognition method research based on semi supervised neighborhood self adaptive linera local tangent space alignment |
| topic | Semi-supervised Neighborhood self-adaptive Linear local tangent space alignment Pattern recognition |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.007 |
| work_keys_str_mv | AT xiexiaohua faultrecognitionmethodresearchbasedonsemisupervisedneighborhoodselfadaptivelineralocaltangentspacealignment AT wangqinghong faultrecognitionmethodresearchbasedonsemisupervisedneighborhoodselfadaptivelineralocaltangentspacealignment |