By Topic

Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval

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

1 Author(s)
Xiaofei He ; State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China

In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels are expensive and the challenge is, thus, to determine which unlabeled samples would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. Particularly, we consider the problem of active learning of a regression model in the context of experimental design. Classical optimal experimental design approaches are based on least square errors over the measured samples only. They fail to take into account the unmeasured samples. In this paper, we propose a novel active learning algorithm which operates over graphs. Our algorithm is based on a graph Laplacian regularized regression model which simultaneously minimizes the least square error on the measured samples and preserves the local geometrical structure of the data space. By constructing a nearest neighbor graph, the geometrical structure of the data space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of data points, which gives us the most amount of information. Experiments demonstrate its superior performance in comparison with conventional algorithms.

Published in:

Image Processing, IEEE Transactions on  (Volume:19 ,  Issue: 1 )