Cart (Loading....) | Create Account
Close category search window
 

Multi-Label Transfer Learning With Sparse Representation

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

4 Author(s)
Yahong Han ; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China ; Fei Wu ; Yueting Zhuang ; Xiaofei He

Due to the visually polysemous barrier, videos and images may be annotated by multiple tags. Discovering the correlations among different tags can significantly help predicting precise labels for videos and images. Many of recent studies toward multi-label learning construct a linear subspace embedding with encoded multi-label information, such that data points sharing many common labels tend to be close to each other in the embedded subspace. Motivated by the advances of compressive sensing research, a sparse representation that selects a compact subset to describe the input data can be more discriminative. In this paper, we propose a sparse multi-label learning method to circumvent the visually polysemous barrier of multiple tags. Our approach learns a multi-label encoded sparse linear embedding space from a related dataset, and maps the target data into the learned new representation space to achieve better annotation performance. Instead of using l1-norm penalty (lasso) to induce sparse representation, we propose to formulate the multi-label learning as a penalized least squares optimization problem with elastic-net penalty. By casting the video concept detection and image annotation tasks into a sparse multi-label transfer learning framework in this paper, ridge regression, lasso, elastic net, and the multi-label extended sparse discriminant analysis methods are, respectively, well explored and compared.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:20 ,  Issue: 8 )

Date of Publication:

Aug. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.