By Topic

Semi-Supervised Learning Algorithm Based on Lie Group

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 $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

2 Author(s)
Hanxiang Xu ; Sch. of Comput. Sci. & Technol., Soochow Univ. Suzhou, Suzhou, China ; Fanzhang Li

Semi-supervised learning is an important research area in machine learning, which is mainly combined with a little labeled training data from reality, studies the data structure and distribution information from the large number of unlabeled data and makes full use of this information to improve the performance of classification algorithms, and researches the symmetry between the labeled and unlabeled samples. Lie Group is the combination of algebraic and geometrical structure by natural, it is the basic method to study the symmetry of the physical problems, so this paper introduces Lie Group to semi-supervised learning, analyzes the relationship between semi-supervised learning and Lie group, uses Lie group's nice algebraic and geometrical structure to denote and analyze data, gives the Semi-Supervised Learning algorithm based on Lie Group, and then in the experiment of predicting drug activity and comparing results with Self training, TSVM, and Co-training, shows the algorithm's feasibility and validity.

Published in:

2009 WRI Global Congress on Intelligent Systems  (Volume:3 )

Date of Conference:

19-21 May 2009