By Topic

Semi-supervised manifold learning based on 2-fold weights

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
$33 $33
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)
M. Fu ; Department of Computer Science and Technology, West Anhui University, Anhui Lu¿an 237012, People's Republic of China ; B. Luo ; M. Kong

In locally linear embedding framework, a semi-supervised manifold learning method based on 2-fold weights is proposed. The basic idea is not only to preserve intra-class local information in the processing of dimensionality reduction but also to predict the label of a data point according to its neighbours. Different from existing approaches, our method finds the k-nearest neighbours of each point in k-multiplicity minimum spanning trees (MST) instead of the complete Euclidean graph. Two-fold weights are learned. One is the reconstruction weights for finding the embedding. The other is the derivative weights for class label propagation. The experimental results on synthetic and real data, multi-class data sets demonstrate the effectiveness of the proposed approach.

Published in:

IET Computer Vision  (Volume:6 ,  Issue: 4 )