DocumentCode
2935038
Title
Linearization of Isomap
Author
Wang, Jing
Author_Institution
Huaqiao Univ., Quanzhou
fYear
2007
fDate
Nov. 28 2007-Dec. 1 2007
Firstpage
766
Lastpage
769
Abstract
The problem of dimensionality reduction arises in many fields of information processing. In this paper, we propose a novel linear dimensionality reduction algorithm called Linear Isomap (Lisomap). It preserves the geodesic distances in the low-dimensional space which is linearly mapped from the high-dimensional space. Numerical examples are given to show the improvement and efficiency of the proposed algorithm.
Keywords
differential geometry; learning (artificial intelligence); linearisation techniques; pattern recognition; ISOMAP; dimensionality reduction; information processing; linearization; Educational institutions; Laplace equations; Machine learning; Machine learning algorithms; Manifolds; Pattern recognition; Principal component analysis; Signal processing; Signal processing algorithms; Space technology; Isomap; Linearization; Manifold learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on
Conference_Location
Xiamen
Print_ISBN
978-1-4244-1447-5
Electronic_ISBN
978-1-4244-1447-5
Type
conf
DOI
10.1109/ISPACS.2007.4446000
Filename
4446000
Link To Document