Neighborhood preserving D-optimal design for active learning and its application to terrain classification

In many real-world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attempts to discover the most informative data points for labeling. The challenge is which unlabeled samples should be labeled...

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Bibliographic Details
Published in:Neural computing & applications 2013-12, Vol.23 (7-8), p.2085-2092
Main Authors: Gu, Yingjie, Jin, Zhong
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geophysics: general, magnetic, electric and thermic methods and properties
Image Processing and Computer Vision
Internal geophysics
Memory organisation. Data processing
Original Article
Pattern recognition. Digital image processing. Computational geometry
Probability and Statistics in Computer Science
Software
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Summary:In many real-world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attempts to discover the most informative data points for labeling. The challenge is which unlabeled samples should be labeled to improve the classifier the most. Classical optimal experimental design algorithms are based on least-square errors over the labeled samples only while the unlabeled points are ignored. In this paper, we propose a novel active learning algorithm called neighborhood preserving D-optimal design. Our algorithm is based on a neighborhood preserving regression model which simultaneously minimizes the least-square error on the measured samples and preserves the neighborhood structure of the data space. It selects the most informative samples which minimize the variance of the regression parameter. We also extend our algorithm to nonlinear case by using kernel trick. Experimental results on terrain classification show the effectiveness of proposed approach.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-1155-3