Zhang, T. and Li, Xuelong and Tao, D. and Yang, J. (2008) Local coordinates alignment (LCA): a novel manifold learning approach. International Journal of Pattern Recognition and Artificial Intelligence 22 (04), pp. 667-690. ISSN 0218-0014.
Abstract
Manifold learning has been demonstrated as an effective way to represent intrinsic geometrical structure of samples. In this paper, a new manifold learning approach, named Local Coordinates Alignment (LCA), is developed based on the alignment technique. LCA first obtains local coordinates as representations of local neighborhood by preserving proximity relations on a patch, which is Euclidean. Then, these extracted local coordinates are aligned to yield the global embeddings. To solve the out of sample problem, linearization of LCA (LLCA) is proposed. In addition, in order to solve the non-Euclidean problem in real world data when building the locality, kernel techniques are utilized to represent similarity of the pairwise points on a local patch. Empirical studies on both synthetic data and face image sets show effectiveness of the developed approaches.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | manifold learning, local coordinates alignment, dimensionality reduction |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 12 Jul 2013 14:03 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7684 |
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