Dimensionality reduction by LPP‐L21
Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers. To alleviate this problem, the authors propose a robust LPP, namel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-08-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0302 |
| _version_ | 1827817482497294336 |
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| author | Shujian Wang Deyan Xie Fang Chen Quanxue Gao |
| author_facet | Shujian Wang Deyan Xie Fang Chen Quanxue Gao |
| author_sort | Shujian Wang |
| collection | DOAJ |
| description | Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers. To alleviate this problem, the authors propose a robust LPP, namely LPP‐L21. LPP‐L21 employs L2‐norm as the distance metric in spatial dimension of data and L1‐norm as the distance metric over different data points. Moreover, the authors employ L1‐norm to construct similarity graph, this helps to improve robustness of algorithm. Accordingly, the authors present an efficient iterative algorithm to solve LPP‐L21. The authors’ proposed method not only well suppresses outliers but also retains LPP's some nice properties. Experimental results on several image data sets show its advantages. |
| first_indexed | 2024-03-12T00:34:50Z |
| format | Article |
| id | doaj.art-185dd47b95324c579a2ca88c9da7eac9 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:34:50Z |
| publishDate | 2018-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-185dd47b95324c579a2ca88c9da7eac92023-09-15T09:48:11ZengWileyIET Computer Vision1751-96321751-96402018-08-0112565966510.1049/iet-cvi.2017.0302Dimensionality reduction by LPP‐L21Shujian Wang0Deyan Xie1Fang Chen2Quanxue Gao3State Key Laboratory of Integrated Services Networks, Xidian University710071Xi'anPeople's Republic of ChinaState Key Laboratory of Integrated Services Networks, Xidian University710071Xi'anPeople's Republic of ChinaState Key Laboratory of Integrated Services Networks, Xidian University710071Xi'anPeople's Republic of ChinaState Key Laboratory of Integrated Services Networks, Xidian University710071Xi'anPeople's Republic of ChinaLocality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers. To alleviate this problem, the authors propose a robust LPP, namely LPP‐L21. LPP‐L21 employs L2‐norm as the distance metric in spatial dimension of data and L1‐norm as the distance metric over different data points. Moreover, the authors employ L1‐norm to construct similarity graph, this helps to improve robustness of algorithm. Accordingly, the authors present an efficient iterative algorithm to solve LPP‐L21. The authors’ proposed method not only well suppresses outliers but also retains LPP's some nice properties. Experimental results on several image data sets show its advantages.https://doi.org/10.1049/iet-cvi.2017.0302dimensionality reductionLPP-L21locality preserving projectionrepresentative linear manifold learning methodsintrinsic data structurerobust LPP |
| spellingShingle | Shujian Wang Deyan Xie Fang Chen Quanxue Gao Dimensionality reduction by LPP‐L21 IET Computer Vision dimensionality reduction LPP-L21 locality preserving projection representative linear manifold learning methods intrinsic data structure robust LPP |
| title | Dimensionality reduction by LPP‐L21 |
| title_full | Dimensionality reduction by LPP‐L21 |
| title_fullStr | Dimensionality reduction by LPP‐L21 |
| title_full_unstemmed | Dimensionality reduction by LPP‐L21 |
| title_short | Dimensionality reduction by LPP‐L21 |
| title_sort | dimensionality reduction by lpp l21 |
| topic | dimensionality reduction LPP-L21 locality preserving projection representative linear manifold learning methods intrinsic data structure robust LPP |
| url | https://doi.org/10.1049/iet-cvi.2017.0302 |
| work_keys_str_mv | AT shujianwang dimensionalityreductionbylppl21 AT deyanxie dimensionalityreductionbylppl21 AT fangchen dimensionalityreductionbylppl21 AT quanxuegao dimensionalityreductionbylppl21 |