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
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;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4956-4
DOI :
10.1109/IHMSC.2014.128