NNNPE: non-neighbourhood and neighbourhood preserving embedding
Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and NPE is a linear approximation to LLE. However...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2133082 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850236231667941376 |
|---|---|
| author | Kaizhi Chen Chengpei Le Shangping Zhong Longkun Guo Ge Xu |
| author_facet | Kaizhi Chen Chengpei Le Shangping Zhong Longkun Guo Ge Xu |
| author_sort | Kaizhi Chen |
| collection | DOAJ |
| description | Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and NPE is a linear approximation to LLE. However, these two algorithms only consider maintaining the neighbour relationship of samples in low-dimensional space and ignore the global features between non-neighbour samples, such as the face shooting angle. Therefore, in order to simultaneously consider the nearest neighbour structure and global features of samples in nonlinear dimensionality reduction, it can be linearly calculated. This work provides a novel linear dimensionality reduction approach named non-neighbour and neighbour preserving embedding (NNNPE). First, we rewrite the objective function of the algorithm LLE based on the principle of our novel algorithm. Second, we introduce the linear mapping to the objective function. Finally, the mapping matrix is calculated by the method of the fast learning Mahalanobis metric. The experimental results show that the method proposed in this paper is effective. |
| format | Article |
| id | doaj-art-151e4b568f644de6a9b68eada2f560ee |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-151e4b568f644de6a9b68eada2f560ee2025-08-20T02:02:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412615262910.1080/09540091.2022.21330822133082NNNPE: non-neighbourhood and neighbourhood preserving embeddingKaizhi Chen0Chengpei Le1Shangping Zhong2Longkun Guo3Ge Xu4College of Computer and Data Science, Fuzhou UniversityCollege of Computer and Data Science, Fuzhou UniversityCollege of Computer and Data Science, Fuzhou UniversityCollege of Computer and Data Science, Fuzhou UniversityMinjiang UniversityManifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and NPE is a linear approximation to LLE. However, these two algorithms only consider maintaining the neighbour relationship of samples in low-dimensional space and ignore the global features between non-neighbour samples, such as the face shooting angle. Therefore, in order to simultaneously consider the nearest neighbour structure and global features of samples in nonlinear dimensionality reduction, it can be linearly calculated. This work provides a novel linear dimensionality reduction approach named non-neighbour and neighbour preserving embedding (NNNPE). First, we rewrite the objective function of the algorithm LLE based on the principle of our novel algorithm. Second, we introduce the linear mapping to the objective function. Finally, the mapping matrix is calculated by the method of the fast learning Mahalanobis metric. The experimental results show that the method proposed in this paper is effective.http://dx.doi.org/10.1080/09540091.2022.2133082machine learningmanifold learningdimensionality reductionneighborhood preserving embedding |
| spellingShingle | Kaizhi Chen Chengpei Le Shangping Zhong Longkun Guo Ge Xu NNNPE: non-neighbourhood and neighbourhood preserving embedding Connection Science machine learning manifold learning dimensionality reduction neighborhood preserving embedding |
| title | NNNPE: non-neighbourhood and neighbourhood preserving embedding |
| title_full | NNNPE: non-neighbourhood and neighbourhood preserving embedding |
| title_fullStr | NNNPE: non-neighbourhood and neighbourhood preserving embedding |
| title_full_unstemmed | NNNPE: non-neighbourhood and neighbourhood preserving embedding |
| title_short | NNNPE: non-neighbourhood and neighbourhood preserving embedding |
| title_sort | nnnpe non neighbourhood and neighbourhood preserving embedding |
| topic | machine learning manifold learning dimensionality reduction neighborhood preserving embedding |
| url | http://dx.doi.org/10.1080/09540091.2022.2133082 |
| work_keys_str_mv | AT kaizhichen nnnpenonneighbourhoodandneighbourhoodpreservingembedding AT chengpeile nnnpenonneighbourhoodandneighbourhoodpreservingembedding AT shangpingzhong nnnpenonneighbourhoodandneighbourhoodpreservingembedding AT longkunguo nnnpenonneighbourhoodandneighbourhoodpreservingembedding AT gexu nnnpenonneighbourhoodandneighbourhoodpreservingembedding |