By Topic

Manifold Learning using Growing Locally Linear Embedding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Junsong Yin ; National Univ. of Defense Technol., Changsha ; Dewen Hu ; Zongtan Zhou

Locally linear embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and growing neural gas (GNG), named growing locally linear embedding (GLLE). First, we address the major limitations of the original LLE: intrinsic dimensionality estimation, neighborhood number selection and computational complexity. Then by embedding the topology learning mechanism in GNG, the proposed GLLE algorithm is able to preserve the global topological structures and hold the geometric characteristics of the input patterns, which make the projections more stable and robust. Theoretical analysis and experimental simulations show that GLLE with global topology preservation tackles the three limitations, gives faster learning procedure and lower reconstruction error, and stimulates the wide applications of manifold learning

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007