By Topic

Supervised Locally Linear Embedding in Tensor Space

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

6 Author(s)
Chang Liu ; Sch. of Comput. Sci., Sichuan Univ., Chengdu, China ; Jiliu Zhou ; Kun He ; YanLi Zhu
more authors

The paper propose a new non-linear dimensionality reduction algorithm based on locally linear embedding called supervised locally linear embedding in tensor space (SLLE/T), in which the local manifold structure within same class are preserved and the separability between different classes is enforced by maximizing distance of each point with its neighbors. To keep structure of data, we introduce tensor representation and reduce SLLE/T into the optimization problem based on HOSVD which is desirable to solve the out of sample problem. We also prove SLLE/T can be united in the graph embedding framework. The comparison experiments on face recognition indicate that SLLE/T outperform most popular dimensionality reduction algorithms both vectorization and tensor version.

Published in:

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on  (Volume:3 )

Date of Conference:

21-22 Nov. 2009