DocumentCode
256695
Title
Landmark-Based Local Patches Alignment Embedding
Author
Jing Chen ; Yang Liu
Author_Institution
Sch. of Phys. & Optoelectron. Eng., Guangdong Univ. of Technol., Guangzhou, China
Volume
2
fYear
2014
fDate
26-27 Aug. 2014
Firstpage
104
Lastpage
107
Abstract
A novel embedding method, called landmark-based local patches alignment embedding (LLPA), is proposed. LLPA first searches a set of landmarks which preserve the global structure of data set well and constructs overlapping patches based on these landmarks. Then, global isometric mapping and multidimensional scale are applied respectively to derive the low-dimensional coordinates of the landmarks and local patches. Finally, we yield the resulting global coordinates by patches alignment technique combined with a set of landmarks in low-dimensional space as reference points.
Keywords
learning (artificial intelligence); LLPA embedding method; global isometric mapping; landmark coordinates; landmark-based local patches alignment embedding; low-dimensional space; manifold learning; multidimensional scale; patches alignment technique; Artificial intelligence; Cybernetics; Educational institutions; Laplace equations; Manifolds; Pattern recognition; Vectors; Manifold learning; local tangent space alignment; multidimensional scale;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4799-4956-4
Type
conf
DOI
10.1109/IHMSC.2014.128
Filename
6911459
Link To Document