Locality constrained dictionary learning for non‐linear dimensionality reduction and classification
In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potentia...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-02-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2015.0482 |
| _version_ | 1827817808397860864 |
|---|---|
| author | Lina Liu Shiwei Ma Ling Rui Jian Lu |
| author_facet | Lina Liu Shiwei Ma Ling Rui Jian Lu |
| author_sort | Lina Liu |
| collection | DOAJ |
| description | In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non‐linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub‐dictionaries are class‐specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large‐scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors’ algorithm outperforms some competing dictionary learning methods. |
| first_indexed | 2024-03-12T00:38:53Z |
| format | Article |
| id | doaj.art-218096608c29439d85fbe393ab5a15a9 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:38:53Z |
| publishDate | 2017-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-218096608c29439d85fbe393ab5a15a92023-09-15T09:02:20ZengWileyIET Computer Vision1751-96321751-96402017-02-01111606710.1049/iet-cvi.2015.0482Locality constrained dictionary learning for non‐linear dimensionality reduction and classificationLina Liu0Shiwei Ma1Ling Rui2Jian Lu3School of Mechatronic Engineering and AutomationShanghai UniversityZhabei District Yanchang Road 149ShanghaiPeople's Republic of ChinaSchool of Mechatronic Engineering and AutomationShanghai UniversityZhabei District Yanchang Road 149ShanghaiPeople's Republic of ChinaSchool of Mechatronic Engineering and AutomationShanghai UniversityZhabei District Yanchang Road 149ShanghaiPeople's Republic of ChinaNational Institute of Occupational Safety and HealthTokyoJapanIn view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non‐linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub‐dictionaries are class‐specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large‐scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors’ algorithm outperforms some competing dictionary learning methods.https://doi.org/10.1049/iet-cvi.2015.0482locality constrained dictionary learning processnonlinear dimensionality reduction methodnonlinear dimensionality classificationincremental dimensionality reduction problemLCDLintrinsic local geometric feature |
| spellingShingle | Lina Liu Shiwei Ma Ling Rui Jian Lu Locality constrained dictionary learning for non‐linear dimensionality reduction and classification IET Computer Vision locality constrained dictionary learning process nonlinear dimensionality reduction method nonlinear dimensionality classification incremental dimensionality reduction problem LCDL intrinsic local geometric feature |
| title | Locality constrained dictionary learning for non‐linear dimensionality reduction and classification |
| title_full | Locality constrained dictionary learning for non‐linear dimensionality reduction and classification |
| title_fullStr | Locality constrained dictionary learning for non‐linear dimensionality reduction and classification |
| title_full_unstemmed | Locality constrained dictionary learning for non‐linear dimensionality reduction and classification |
| title_short | Locality constrained dictionary learning for non‐linear dimensionality reduction and classification |
| title_sort | locality constrained dictionary learning for non linear dimensionality reduction and classification |
| topic | locality constrained dictionary learning process nonlinear dimensionality reduction method nonlinear dimensionality classification incremental dimensionality reduction problem LCDL intrinsic local geometric feature |
| url | https://doi.org/10.1049/iet-cvi.2015.0482 |
| work_keys_str_mv | AT linaliu localityconstraineddictionarylearningfornonlineardimensionalityreductionandclassification AT shiweima localityconstraineddictionarylearningfornonlineardimensionalityreductionandclassification AT lingrui localityconstraineddictionarylearningfornonlineardimensionalityreductionandclassification AT jianlu localityconstraineddictionarylearningfornonlineardimensionalityreductionandclassification |