Title :
Local and Global Structures Preserving Projection
Author :
Cheng, Hao ; Hua, Kien A. ; Vu, Khanh
Author_Institution :
Univ. of Central Florida, Orlando
Abstract :
In this paper, we propose Local and Global Structures Preserving Projection (LGSPP), which is to find a small set of projection directions so as to properly preserve the local and global structures for a given set of data. Specifically, for each point in the dataset, its local neighborhood is extracted as well as a set of sampled points far away from this point, which characterize the global structure. The embedding minimizes the distances of the points in each local neighborhood while dispersing them far apart from their corresponding remote points. In this way, the local-global relationships between data points are well kept.
Keywords :
learning (artificial intelligence); global structures preserving projection; local neighborhood; local structures preserving projection; manifold learning; Artificial intelligence; Computer science; Data mining; Euclidean distance; Large-scale systems; Nearest neighbor searches; Nonlinear distortion; Principal component analysis; Proposals; Robustness;
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
Print_ISBN :
978-0-7695-3015-4
DOI :
10.1109/ICTAI.2007.145