DocumentCode
2485857
Title
Local and Global Structures Preserving Projection
Author
Cheng, Hao ; Hua, Kien A. ; Vu, Khanh
Author_Institution
Univ. of Central Florida, Orlando
Volume
2
fYear
2007
fDate
29-31 Oct. 2007
Firstpage
362
Lastpage
365
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location
Patras
ISSN
1082-3409
Print_ISBN
978-0-7695-3015-4
Type
conf
DOI
10.1109/ICTAI.2007.145
Filename
4410406
Link To Document