Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

11

ISNN
2007
Springer

favoriteEmaildiscussreport

117views Neural Networks» more ISNN 2007»

Extensions of Manifold Learning Algorithms in Kernel Feature Space

13 years 10 months ago

Extensions of Manifold Learning Algorithms in Kernel Feature Space

Download homepage.fudan.edu.cn

Manifold learning algorithms have been proven to be capable of discovering some nonlinear structures. However, it is hard for them to extend to test set directly. In this paper, a simple yet effective extension algorithm called PIE is proposed. Unlike LPP, which is linear in nature, our method is nonlinear. Besides, our method will never suffer from the singularity problem while LPP and KLPP will. Experimental results of data visualization and classification validate the effectiveness of our proposed method.

Yaoliang Yu, Peng Guan, Liming Zhang

Real-time Traffic

Algorithm Called Pie | ISNN 2007 | Learning Algorithms | Nonlinear Structures |

claim paper

Related Content

» Learning a kernel matrix for nonlinear dimensionality reduction

» Feature extraction by learning Lorentzian metric tensor and its extensions

» Appearance Manifold of Facial Expression

» Connecting the OutofSample and PreImage Problems in Kernel Methods

» Local manifold matching for face recognition

» Nonisometric manifold learning analysis and an algorithm

» Feature selection in a kernel space

» Manifold Regularization A Geometric Framework for Learning from Labeled and Unlabeled Exam...

» Kernel Autoassociator with Applications to Visual Classification

Post Info
More Details (n/a)

Added	08 Jun 2010
Updated	08 Jun 2010
Type	Conference
Year	2007
Where	ISNN
Authors	Yaoliang Yu, Peng Guan, Liming Zhang

Comments (0)