Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31216
Title: Local Coordinates Alignment (LCA) : a novel manifold learning approach
Authors: Zhang, T
Li, X
Tao, D
Yang, J
Keywords: Dimensionality reduction
Local coordinates alignment
Manifold learning
Issue Date: 2008
Publisher: World Scientific
Source: International journal of pattern recognition and artificial intelligence, 2008, v. 22, no. 4, p. 667-690 How to cite?
Journal: International journal of pattern recognition and artificial intelligence 
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.
URI: http://hdl.handle.net/10397/31216
ISSN: 0218-0014
EISSN: 1793-6381
DOI: 10.1142/S0218001408006478
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

19
Last Week
0
Last month
1
Citations as of Sep 19, 2017

WEB OF SCIENCETM
Citations

16
Last Week
0
Last month
2
Citations as of Sep 20, 2017

Page view(s)

71
Last Week
4
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.